NER

Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition (N18-1)

Challenge:	TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach:	They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome:	The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.

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Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu

Entity Recognition at First Sight: Improving NER with Eye Movement Information (N19-1)

Challenge:	NER is a fundamental problem for medical text mining because of the difference of specialties and cost of human annotation.
Approach:	They propose a label-aware double transfer learning framework for medical NER from electronic medical records.
Outcome:	The proposed framework improves accuracy over strong baselines on 12 cross-specialty NER tasks.

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Nora Hollenstein, Ce Zhang

PolyMinder: A Support System for Entity Annotation and Relation Extraction in Polymer Science Documents (2025.coling-demos)

Challenge:	Previous studies have shown eye-tracking data can be used to improve natural language processing models.
Approach:	They leverage eye movement features from three corpora with recorded gaze information to augment a neural model for named entity recognition with gaze embeddings.
Outcome:	The proposed model outperforms baseline models on both individual datasets and in cross-domain settings.

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Truong Dinh Do, An Hoang Trieu, Van-Thuy Phi, Minh Le Nguyen, Yuji Matsumoto

Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)

Challenge:	Automated Named Entity Recognition (NER) and Relation Extraction (RE) models are tailored to the polymer domain.
Approach:	They propose to automate the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations.
Outcome:	The proposed system streamlines the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations.

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Yash Agarwal, Devansh Batra, Ganesh Bagler

Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition (2021.acl-short)

Challenge:	Named entities pose a unique challenge to traditional methods of language modeling.
Approach:	They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources.
Outcome:	The proposed model is based on 158,473 cooking recipes from public sources.

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Chun Chen, Fang Kong

AutoRC: Improving BERT Based Relation Classification Models via Architecture Search (2021.acl-srw)

Challenge:	Existing approaches to Chinese Named Entity Recognition (NER) lack explicit word boundary and tenses information.
Approach:	They propose a boundary enhanced approach for Chinese Named Entity Recognition . they add an additional Graph Attention Network(GAT) layer to capture internal dependency of phrases .
Outcome:	The proposed approach improves Chinese Named Entity Recognition (NER) on OntoNotes and Weibo corpora.

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Wei Zhu

A Deep Learning-Based System for PharmaCoNER (D19-57)

Challenge:	Existing literature on relation classification models shows no consensus on optimal architecture .
Approach:	They propose a search space for BERT based relation classification models and employ an ENAS method to find better architectures.
Outcome:	The proposed method can find better architectures than baseline models on eight benchmark RC tasks.

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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

Information Retrieval

Challenge:	Efficient access to mentions of clinical entities is very important for using clinical text.
Approach:	They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 .
Outcome:	The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2.

T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (2021.eacl-demos)

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Asahi Ushio, Jose Camacho-Collados

Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages (2024.naacl-srw)

Challenge:	Language model (LM) pretraining has led to consistent improvements in many downstream tasks, including named entity recognition (NER).
Approach:	They propose a Python library for NER LM finetuning that facilitates cross-domain and cross-lingual generalization of LMs finetuned on NER.
Outcome:	The proposed library outperforms LMs trained on NERs in cross-domain and cross-lingual generalization tests on nine datasets.

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Sankalp Bahad, Pruthwik Mishra, Parameswari Krishnamurthy, Dipti Sharma

Towards a Standardized Dataset on Indonesian Named Entity Recognition (2020.aacl-srw)

Challenge:	Named Entity Recognition (NER) is a useful component in NLP applications.
Approach:	They propose to use annotated named entity corpora to classify a given entity into a category within a textual document.
Outcome:	The proposed model achieves an F1 score of 0.80 on an unseen dataset for Indian languages.

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Siti Oryza Khairunnisa, Aizhan Imankulova, Mamoru Komachi

DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature (D19-57)

Challenge:	Named entity recognition (NER) tasks in the Indonesian language are still lacking data for the majority of languages, including Indonesian.
Approach:	They re-annotated an open dataset with 2,000 sentences and compared the results with a bidirectional long short-term memory and conditional random field approach.
Outcome:	The proposed approach improved the prediction score and consistent organization tag for the Indonesian language.

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Feifan Liu, Xiaoyu Zheng, Bo Wang, Catarina Kiefe

RACAI’s System at PharmaCoNER 2019 (D19-57)

Challenge:	Identifying and understanding the pathogenesis of genetic diseases is an essential task.
Approach:	They propose a joint deep learning model for gene mutation-disease knowledge extraction that adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition and relation extraction.
Outcome:	The proposed model achieves the average score of 0.45 on recognizing gene activities and disease entities and the average F1 score of 0.3 on extracting relations, ranking 1st in the AGAC RE task.

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Radu Ion, Vasile Florian Păiș, Maria Mitrofan

Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (2023.emnlp-industry)

Challenge:	RACAI researchers develop named entity recognition systems for Romanian language . current system is language-independent and can be improved by using language-dependent resources .
Approach:	They propose to train a named entity recognition system for Romanian language . they propose to use a gazetteer-based baseline and a RNN-based NER system .
Outcome:	The proposed system is language independent, provided language-dependent resources exist . the proposed system can detect entities with four labels: anatomical parts, disorders, medical procedures and chemical compounds .

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Wei-Te Chen, Keiji Shinzato, Naoki Yoshinaga, Yandi Xia

Question Answering

Challenge:	scalability of attribute-value extraction (AVE) task is key for a large number of products . a question-answering (QA)-based approach is better for AVE, but requires a larger number of classes to be scalable.
Approach:	They propose a question-answering-based approach that additionally inputs the target attribute as a query to extract its values.
Outcome:	The proposed approach outperforms a classical approach on real-word e-commerce datasets in accuracy and speed.

Modularized Interaction Network for Named Entity Recognition (2021.acl-long)

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Fei Li, Zheng Wang, Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu, Guoxiu He, Meihuizi Jia

A Named Entity Recognition Shootout for German (P18-2)

Challenge:	Named Entity Recognition (NER) models focus on word-level information, while segment-based models focus only on word level information.
Approach:	They propose a Modularized Interaction Network (MIN) model which utilizes both word-level information and segment-level dependencies.
Outcome:	The proposed model outperforms the current state-of-the-art models on three NER benchmark datasets.

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Martin Riedl, Sebastian Padó

Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)

Challenge:	Named entity recognition and classification (NER) is a central component in many natural language processing pipelines.
Approach:	They propose to build a model for German named entity recognition that performs at the state of the art for both contemporary and historical texts.
Outcome:	The proposed model outperforms the CRF and BiLSTM on large and small datasets.

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Pankaj Gupta, Usama Yaseen, Hinrich Schütze

TMR: Evaluating NER Recall on Tough Mentions (2021.eacl-srw)

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature.
Approach:	They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages.
Outcome:	The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text.

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Jingxuan Tu, Constantine Lignos

Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition (2023.acl-srw)

Challenge:	a NER evaluation tool is available via a repository.
Approach:	They propose to use Tough Mentions Recall to supplement traditional named entity recognition evaluation by examining recall on specific subsets of ”tough” mentions.
Outcome:	The proposed metrics enable differentiation between otherwise similar-scoring systems and identify patterns in performance that would go unnoticed from overall precision, recall, and F1.

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Sakura Imai, Daisuke Kawahara, Naho Orita, Hiromune Oda

Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts (2022.aacl-main)

Challenge:	Existing studies have used descriptive typological features and a coarse language family classification as baselines for language clustering.
Approach:	They propose two types of language groupings based on morpho-syntactic features in a nominal domain and one based upon a head parameter.
Outcome:	The proposed methods outperform state-of-the-art embedding-based models in multilingual named entity recognition (NER) . their results suggest that theoretical linguistics plays a significant role in multi-lingual learning tasks.

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Asahi Ushio, Francesco Barbieri, Vitor Sousa, Leonardo Neves, Jose Camacho-Collados

Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data (P19-2)

Challenge:	Named Entity Recognition (NER) is a longstanding NLP task that consists of identifying an entity in a sentence or document.
Approach:	They construct a dataset of seven entity types annotated over 11,382 tweets . they provide a set of language model baselines and analyze the performance of the model .
Outcome:	The proposed dataset contains seven entity types annotated over 11,382 tweets . the authors focus on short-term degradation of NER models over time and strategies to fine-tune a language model over different periods .

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Vamshi Krishna Srirangam, Appidi Abhinav Reddy, Vinay Singh, Manish Shrivastava

Audio De-identification - a New Entity Recognition Task (N19-2)

Challenge:	Named Entity Recognition (NER) is a subtask of Information Extraction in NLP.
Approach:	They present a Telugu-English code-mixed corpus with the corresponding named entity tags.
Outcome:	The proposed model scored 0.96, 0.94 and 0.95 on a Telugu-English code-mixed corpus.

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Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias

Speech & Audio in NLP

Challenge:	Named Entity Recognition (NER) is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor.
Approach:	They propose to use Named Entity Recognition (NER) to detect audio spans with entity mentions in medical records and then use it to evaluate the results.
Outcome:	The proposed pipeline is based on a large labeled segment of the Switchboard and Fisher audio datasets and compares it with a benchmark.

Joint Learning of Named Entity Recognition and Entity Linking (P19-2)

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Pedro Henrique Martins, Zita Marinho, André F. T. Martins

NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension (2022.naacl-industry)

Challenge:	Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected .
Approach:	They perform joint learning of named entity recognition and entity linking to leverage their relatedness.
Outcome:	The proposed model achieves competitive results with the state-of-the-art in both NER and EL tasks.

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Anubhav Shrimal, Avi Jain, Kartik Mehta, Promod Yenigalla

UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition (D19-57)

Challenge:	Named Entity Recognition (NER) is a task of locating and classifying entities mentioned in unstructured text into predefined categories.
Approach:	They propose to use a BERT-based multi-question MRC task where multiple questions (one question per entity) are considered at the same time for a single text.
Outcome:	The proposed architecture leads to 2.5 times faster training and 2.3 times faster inference on three NER datasets.

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Lenz Furrer, Joseph Cornelius, Fabio Rinaldi

AdminSet and AdminBERT: a Dataset and a Pre-trained Language Model to Explore the Unstructured Maze of French Administrative Documents (2025.coling-main)

Challenge:	CRAFT shared task 2019: concept recognition using named entity recognition and normalization . a biLSTM-based network and a transformer system were used to tackle both tasks in a single model .
Approach:	They propose two different neural approaches to concept recognition . they propose a BiLSTM-based network and a bioBERT-based system for NER and normalization .
Outcome:	The proposed systems model the task as a sequence labeling problem.

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Thomas Sebbag, Solen Quiniou, Nicolas Stucky, Emmanuel Morin

Enhance Robustness of Sequence Labelling with Masked Adversarial Training (2020.findings-emnlp)

Challenge:	Pre-trained language models are used to analyze documents but administrative texts are unstructured and do not perform well.
Approach:	They propose a French pre-trained language model for the administrative domain . they compare it with a general domain language model and a large language model .
Outcome:	The proposed model improves performance on administrative and general domains.

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Luoxin Chen, Xinyue Liu, Weitong Ruan, Jianhua Lu

SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)

Challenge:	Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness.
Approach:	They propose to use adversarial training to improve robustness from contextual information in sequence labelling tasks by masking or replacing some words in the sentence.
Outcome:	The proposed method shows significant improvements on accuracy and robustness of sequence labelling on CoNLL 2000 and 2003 benchmarks.

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Yuxuan Feng, Qian Chen, Qianyou Wu, Xin Guo, Suge Wang

CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset (2021.emnlp-demo)

Challenge:	Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability.
Approach:	They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation.
Outcome:	The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods .

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Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi

CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)

Challenge:	Existing crowd annotation tools for named entity recognition (NER) focus on efficiency and don't consider consistency of datasets.
Approach:	They propose a crowd annotation platform for Chinese named entity recognition (NER) CroAno provides a systematic solution for improving label consistency of Chinese NER datasets.
Outcome:	The proposed platform improves label consistency of Chinese NER datasets.

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Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei

A Boundary-aware Neural Model for Nested Named Entity Recognition (D19-1)

Challenge:	Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data.
Approach:	They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model.
Outcome:	The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance.

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Changmeng Zheng, Yi Cai, Jingyun Xu, Ho-fung Leung, Guandong Xu

Named Entity Recognition in Industrial Tables using Tabular Language Models (2022.emnlp-industry)

Challenge:	Existing methods for named entity recognition ignore nested entities . a boundary-aware neural model can locate entities precisely by detecting boundaries .
Approach:	They propose a boundary-aware neural model for nested named entity recognition which leverages entity boundaries to predict entity categorical labels.
Outcome:	The proposed model outperforms state-of-the-art methods on GENIA dataset . it captures dependencies of entity boundaries and categorical labels, which helps to improve identifying entities.

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Aneta Koleva, Martin Ringsquandl, Mark Buckley, Rakeb Hasan, Volker Tresp

Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition (2021.tacl-1)

Challenge:	Table transformers are used for encoding tabular data but are not yet used in industrial applications.
Approach:	They propose a dedicated table data augmentation strategy based on available domain-specific knowledge graphs to enhance the performance of transformer-based models.
Outcome:	The proposed model outperforms baseline models and its inductive bias is vital for convergence of transformer-based models.

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Abbas Ghaddar, Philippe Langlais, Ahmad Rashid, Mehdi Rezagholizadeh

Leveraging HTML in Free Text Web Named Entity Recognition (2020.coling-main)

Challenge:	Name Regularity Bias is a problem in NER models that use contextual information to predict the type of an ambiguous entity.
Approach:	They propose a model-agnostic training method that adds learnable adversarial noise to some entity mentions to improve their accuracy.
Outcome:	The proposed method outperforms feature-based models on name regularity bias . it adds learnable adversarial noise to some entity mentions, leading to gains .

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Colin Ashby, David Weir

Familarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data (2025.naacl-long)

Challenge:	Named Entity Recognition (NER) is the identification of the proper names of objects.
Approach:	They compare HTML tags discarded in free text Named Entity Recognition from Web pages . they find an increased F1 performance for Text+Tags of between 0.9% and 13.2% .
Outcome:	The proposed method improves F1 performance over datasets, variants and models.

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Jonas Golde, Patrick Haller, Max Ploner, Fabio Barth, Nicolaas Paul Jedema, Alan Akbik

Structure and Label Constrained Data Augmentation for Cross-domain Few-shot NER (2023.findings-emnlp)

Challenge:	Current research relies on large synthetic datasets to train zero-shot named entity recognition models.
Approach:	They propose a metric that captures the semantic similarity between entity types in training and evaluation to estimate label shift.
Outcome:	The proposed metric captures semantic similarity between entity types in training and evaluation, and their frequency in training data to provide an estimate of label shift.

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Jingyi Zhang, Ying Zhang, Yufeng Chen, Jinan Xu

A Study of the Importance of External Knowledge in the Named Entity Recognition Task (P18-2)

Challenge:	Named entity recognition (NER) tasks require large datasets with accurate annotations that are labor-intensive and time-consuming.
Approach:	They propose a method to leverage domain gaps to model cross-domain few-shot named entity recognition (NER) NER is a natural language processing task to detect entity mentions and classify them into predefined labels .
Outcome:	The proposed method achieves state-of-the-art or competitive results on standard datasets.

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Dominic Seyler, Tatiana Dembelova, Luciano Del Corro, Johannes Hoffart, Gerhard Weikum

Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)

Challenge:	Existing studies have shown that external knowledge is important for Named Entity Recognition .
Approach:	They propose a modular framework that divides knowledge into four categories according to depth . they show the effects when incrementally adding deeper knowledge .
Outcome:	The proposed framework outperforms agnostic frameworks with more external knowledge . the proposed frameworks outperformed agrarian frameworks on two standard datasets .

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Luoxin Chen, Francisco Garcia, Varun Kumar, He Xie, Jianhua Lu

skweak: Weak Supervision Made Easy for NLP (2021.acl-demo)

Challenge:	Obtaining human annotation is expensive and time-consuming process.
Approach:	They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks.
Outcome:	The proposed pipeline can be used to improve natural language understanding tasks.

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Pierre Lison, Jeremy Barnes, Aliaksandr Hubin

BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)

Challenge:	skweak is a Python-based toolkit for NLP developers to use weak supervision . labelled data remains a scarce resource in many practical NLP scenarios .
Approach:	They present a Python-based toolkit that allows NLP developers to use weak supervision . skweak is designed to facilitate the use of weak supervision for NLP tasks .
Outcome:	skweak is a Python-based toolkit that facilitates weak supervision . the toolkit provides a simple interface to apply labels to a large corpus of text data .

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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid

OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description (2025.findings-naacl)

Challenge:	Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent.
Approach:	They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse.
Outcome:	The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage.

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Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, Francesco Candoli

Learning Nested Named Entity Recognition from Flat Annotations (2026.eacl-srw)

Challenge:	Biomedical Named Entity Recognition (BioNER) is a computationally expensive and limited tool . specialized 7B NER LLMs and GPT-4o can't match textual spans with entity types .
Approach:	They propose a lightweight BERT-based cross-encoder architecture that can identify any biomedical entity using only its description.
Outcome:	The proposed system outperforms existing models that match textual spans with entity types rather than descriptions on biomedical benchmarks.

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Igor Rozhkov, Natalia V Loukachevitch

Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model (2024.emnlp-industry)

Challenge:	Named entity recognition (NER) requires expensive multi-level annotation.
Approach:	They evaluate four approaches to learning nested structure from flat annotations alone . on NEREL, a Russian benchmark, they find the best method achieves 26.37% inner F1 .
Outcome:	The proposed method closes 40% of the gap to full nested supervision on a Russian benchmark with 29 entity types where 21% of entities are nest.

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Subhadip Nandi, Neeraj Agrawal

Where do LLMs currently stand on biomedical NER in both clean and noisy settings ? (2026.findings-eacl)

Challenge:	Existing approaches to named entity recognition are domain specific and require a domain specific architecture.
Approach:	They propose a retrieval augmented large language model for Named Entity Recognition . the model uses word-embedding over sentence-level embedding to fine tune .
Outcome:	The proposed model outperforms existing models on the CrossNER dataset.

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Christophe Ye, Cassie S. Mitchell

TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (2022.findings-emnlp)

Challenge:	despite advances in medicine, many diseases remain without effective treatments . clinical meta-analysis is essential for drug discovery and clinical research .
Approach:	They investigate the performance of large language models (LLMs) on biomedical NER tasks . findings suggest LLMs exhibit a notable degree of robustness to noise .
Outcome:	The proposed models are closing the performance gap with BERT-based models and demonstrate particular strengths in low-data settings.

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Yichun Zhao, Jintao Du, Gongshen Liu, Huijia Zhu

Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)

Challenge:	Existing methods for named entity recognition are limited by noise in translation . Existing approaches to named entities recognition are mainly based on labeled data .
Approach:	They propose a framework to mitigate lexical and syntactic errors of translated data . they propose to use multi-level adversarial learning and multi-model knowledge distillation to mitigate noise .
Outcome:	The proposed framework mitigates lexical and syntactic errors of translated data . it achieves competitive performance to state-of-the-art models .

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Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura

Named Entity Recognition for Chinese biomedical patents (2020.coling-main)

Challenge:	In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks.
Approach:	They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis.
Outcome:	The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network.

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Yuting Hu, Suzan Verberne

Influence Functions for Sequence Tagging Models (2022.findings-emnlp)

Challenge:	Existing attempts to address NER for Chinese biomedical texts have been limited due to the amount of Chinese biomedicine discoveries being patented.
Approach:	They train and evaluate Chinese biomedical patents NER models based on BERT . their model is optimized for Chinese bio-patent data and scored an F1 .
Outcome:	The proposed model achieves an F1 score of 0.540.15 for Chinese biomedical patent data.

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Sarthak Jain, Varun Manjunatha, Byron Wallace, Ani Nenkova

SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes (2025.acl-industry)

Challenge:	Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling are standard tasks in NLP, but there has been little work on interpretability methods for sequence taging.
Approach:	They propose to extend influence functions to sequence tagging tasks by identifying noisy annotations in NER corpora.
Outcome:	The proposed methods are able to identify noisy annotations in NER corpora and are scalable.

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Nicole Ren, James Teo

An Empirical Study on Fine-Grained Named Entity Recognition (C18-1)

Challenge:	Named Entity Recognition (NER) for low-resource variants of English remains challenging, as most models are trained on datasets predominantly focused on American or British English.
Approach:	They propose a new output format for Named Entity Recognition (NER) that achieves a three-fold reduction in inference time compared to JSON format.
Outcome:	The proposed output format achieves a three-fold reduction in inference time on average compared to JSON format, which is widely used for structured outputs.

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Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine

Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons (2020.findings-emnlp)

Challenge:	Named entity recognition (NER) is a well studied topic in natural language processing.
Approach:	They propose to remove the CNN layer and use dictionary and category embeddings to improve Japanese FG-NER performance.
Outcome:	The proposed method improves Japanese FG-NER F-score from 66.76% to 75.18%.

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Minlong Peng, Ruotian Ma, Qi Zhang, Lujun Zhao, Mengxi Wei, Changlong Sun, Xuanjing Huang

Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models (2022.emnlp-main)

Challenge:	Existing named entity recognition systems require large scale labeled data to perform, while annotation of NER data is laborious and time-consuming.
Approach:	They propose to adjust an existing named entity recognition system to recognize entity types not defined in the system.
Outcome:	The proposed method can be quickly adjusted to a named entity recognition system.

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Aniruddha Mahapatra, Sharmila Reddy Nangi, Aparna Garimella, Anandhavelu N

Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework (2022.emnlp-industry)

Challenge:	Existing methods to perform named entity recognition (NER) on unlabeled data are difficult to obtain in low-resource domains.
Approach:	They propose ways to use unlabeled data for pretraining to improve performance in downstream tasks.
Outcome:	The proposed methods outperform models trained on unlabeled data on seven domains.

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Wonjin Yoon, Richard Jackson, Elliot Ford, Vladimir Poroshin, Jaewoo Kang

Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)

Challenge:	a low overall error rate is unavoidable in the field of bioNLP, argues a new study.
Approach:	They propose a highly extensible open source framework that supports BioNLP for the pharmaceutical sector.
Outcome:	The proposed framework is extensible and scalable and can support BioNLP for the pharmaceutical sector.

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Yucheng Wang, Bowen Yu, Hongsong Zhu, Tingwen Liu, Nan Yu, Limin Sun

Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)

Challenge:	Existing methods for named entity recognition break the recognition process into several sequential steps.
Approach:	They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it .
Outcome:	Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model.

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Juncheng Wan, Dongyu Ru, Weinan Zhang, Yong Yu

Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)

Challenge:	Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text.
Approach:	They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation.
Outcome:	The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities.

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Muhammad Khalifa, Muhammad Abdul-Mageed, Khaled Shaalan

NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)

Challenge:	Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data.
Approach:	They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy.
Outcome:	The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging).

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Xinpeng Zhang, Ming Tan, Jingfan Zhang, Wei Zhu

MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)

Challenge:	Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding.
Approach:	They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding.
Outcome:	The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks.

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David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei

Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)

Challenge:	(2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results.
Approach:	They propose to create a dataset for named entity recognition (NER) in ten African languages.
Outcome:	The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP.

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Mayank Kulkarni, Debanjan Mahata, Ravneet Arora, Rajarshi Bhowmik

Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)

Challenge:	Prior work has referred to extractive (part of document) or abstractive (not part of document).
Approach:	They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings.
Outcome:	The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks.

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Jinzhong Ning, Yuanyuan Sun, Bo Xu, Zhihao Yang, Ling Luo, Hongfei Lin

Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs (2025.findings-naacl)

Challenge:	Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks.
Approach:	They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités.
Outcome:	The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them.

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Itai Mondshine, Tzuf Paz-Argaman, Reut Tsarfaty

Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (D19-1)

Challenge:	Current LLMs are primarily trained on English data but also include data from other languages.
Approach:	They propose to use a pre-translation strategy to translate a task prompt into English before inference . they use 'a modular entity' that could be translated into four different languages .
Outcome:	The proposed strategies are based on a set of pre-trained data across 35 languages covering both low and high-resource languages.

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Shijie Wu, Mark Dredze

”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer (2022.findings-naacl)

Challenge:	Pretrained contextual representation models have pushed forward the state-of-the-art on many NLP tasks.
Approach:	They propose to use a model that is pretrained on 104 languages for cross-lingual transfer.
Outcome:	The proposed model performs well on 5 NLP tasks covering 39 languages from various language families.

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Shanu Kumar, Sandipan Dandapat, Monojit Choudhury

Exploring Cross-sentence Contexts for Named Entity Recognition with BERT (2020.coling-main)

Challenge:	Existing approaches for few-shot transfer show significant gain over zero-shot transfers . language resource distribution is skewed across the world's languages . proposed methods use multiple measures such as data entropy and gradient embedding .
Approach:	They propose a loss embedding method for sequence labeling tasks that induces diversity and uncertainty sampling similar to gradient embeddment.
Outcome:	The proposed methods outperform baseline methods for POS tagging, NER, and NLI tasks for up to 20 languages.

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Jouni Luoma, Sampo Pyysalo

Pooled Contextualized Embeddings for Named Entity Recognition (N19-1)

Challenge:	Named entity recognition (NER) is often addressed as a sequence classification task with each input consisting of one sentence of text.
Approach:	They propose a method to combine different predictions from multiple sentences in input samples to increase NER performance.
Outcome:	The proposed method improves on the state-of-the-art NER results on English, Dutch, and Finnish and achieves the best reported BERT-based results on German.

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Alan Akbik, Tanja Bergmann, Roland Vollgraf

Identifying Named Entities as they are Typed (2021.eacl-main)

Challenge:	Contextual string embeddings are a recent type of word embeddable that are useful for sequence labeling tasks.
Approach:	They propose a method that dynamically aggregates contextualized embeddings of each unique string . they then use a pooling operation to distill a ”global” word representation from all contextualized instances .
Outcome:	The proposed method improves state-of-the-art for named entity recognition tasks.

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Ravneet Arora, Chen-Tse Tsai, Daniel Preotiuc-Pietro

A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)

Challenge:	Named Entity Recognition (NER) systems are not applicable to systems that process text in real time as the text is typed.
Approach:	They propose a new experimental setup for evaluating Named Entity Recognition systems that evaluates named entities as they are typed on a sentence level . they propose to adapt existing evaluation setups to suit the new setup .
Outcome:	The proposed setup shows that the best systems that are evaluated on each token after its typed reach performance within 1–5 F1 points of systems that were evaluated at the end of the sentence.

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Elias Stengel-Eskin, Tzu-ray Su, Matt Post, Benjamin Van Durme

Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders (2025.acl-short)

Challenge:	a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems .
Approach:	They propose a discriminative word alignment model which integrates into a Transformer-based machine translation model.
Outcome:	The proposed model performs better on Chinese and Arabic alignments than standard models.

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Taku Oi, Makoto Miwa

A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)

Challenge:	Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models.
Approach:	They propose to integrate CVAE into a span-based Named Entity Recognition model.
Outcome:	The proposed method achieves better performance on the BioRED dataset.

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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)

Challenge:	Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing.
Approach:	They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models.
Outcome:	The proposed model improves on real-world and synthetic datasets compared with baselines.

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Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia

Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection (2023.findings-eacl)

Challenge:	Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks.
Approach:	They propose a multi-task neural model to jointly extract entities and negations from medical reports.
Outcome:	The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.

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Tolulope Ogunremi, Dan Jurafsky, Christopher Manning

Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations (D19-1)

Challenge:	AfriBERTa shows that training transformer models from scratch on 1GB of data from many unrelated African languages outperforms massively multilingual models on downstream NLP tasks.
Approach:	They propose that training on smaller amounts of data but from related languages could match the performance of models trained on large, unrelated data.
Outcome:	The proposed model outperforms models trained on large, unrelated datasets on downstream NLP tasks.

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Zuyi Bao, Rui Huang, Chen Li, Kenny Zhu

A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)

Challenge:	Existing approaches to cross-lingual sequence labeling require bilingual resources and require linguistic knowledge.
Approach:	They propose a multilingual language model with deep semantic Alignment to generate language-independent representations for cross-lingual sequence labeling.
Outcome:	The proposed model achieves state-of-the-art NER and POS performance across European languages and on distant language pairs such as English and Chinese.

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Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei, Xuanjing Huang

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)

Challenge:	Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure.
Approach:	They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features.
Outcome:	The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics.

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Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Shiyuan Xiao, Yuanxin Ouyang, Wenge Rong, Jianxin Yang, Zhang Xiong

Challenge:	Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty.
Approach:	They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges.
Outcome:	The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods.

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Named Entity Recognition for Social Media Texts with Semantic Augmentation (2020.emnlp-main)

Challenge:	Named entity recognition (NER) tasks are a fundamental challenge for name recognition tasks that aim to reduce the boundary error when entities become longer.
Approach:	They propose a similarity based auxiliary classifier which can distinguish entity words from non-entity words by using vectors to indicate tags.
Outcome:	Empirical results show that the proposed classifier can perform better than baseline approaches.

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Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai

A Novel Three-stage Framework for Few-shot Named Entity Recognition (2024.lrec-main)

Challenge:	Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts.
Approach:	They propose a neural-based approach to named entity recognition for social media texts . they obtain augmented semantic information from a large-scale corpus and encode it .
Outcome:	The proposed approach outperforms existing approaches on three social media datasets.

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Shengjie Ji, Fang Kong

GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input (2021.naacl-main)

Challenge:	Existing methods for Named Entity Recognition (NER) rely on labeled data, but data scarcity is a major challenge.
Approach:	They propose a framework for Few-shot Named Entity Recognition that can learn from limited labeled data and generalize to new domains.
Outcome:	The proposed framework surpasses existing methods on several benchmarks.

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Tao Meng, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi

Representation Learning

Challenge:	Named Entity Recognition (NER) is difficult in real-world settings due to short texts, emerging entities, and complex entities.
Approach:	They propose a flexible Gazetteer Representation encoder and a Mixture-of-Experts gating network for gazetteer knowledge integration.
Outcome:	The proposed approach shows large gains (up to +49% F1) in recognizing difficult entities compared to baselines.

MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition (2021.acl-long)

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Shuang Wu, Xiaoning Song, Zhenhua Feng

FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition (2026.findings-eacl)

Challenge:	Named Entity Recognition (NER) is a sequence tagging task that extracts named entities from unstructured text.
Approach:	They propose to integrate Chinese character features with radical-level embedding to improve Chinese NER by integrating Chinese character information.
Outcome:	The proposed method can improve Chinese Named Entity Recognition (NER) on well-known datasets.

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Jonas Golde, Patrick Haller, Alan Akbik

An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition (2023.acl-short)

Challenge:	Named entity recognition (NER) is the task of identifying tokens that belong to a predefined set of classes such as "person" or "location"
Approach:	They propose a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts.
Outcome:	The proposed model achieves comparable or improved performance in English, Thai, and Swahili despite being trained on 19x less data than strong baselines.

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Hang Yan, Yu Sun, Xiaonan Li, Xipeng Qiu

Improving Model Generalization: A Chinese Named Entity Recognition Case Study (2021.acl-short)

Challenge:	Named entity recognition (NER) is the task to detect and classify entity spans in text.
Approach:	They propose to use Convolutional Neural Network to model spatial relations in NER . they use three commonly used nested NER datasets to compare their results .
Outcome:	The proposed model outperforms several proposed methods with the same pre-trained encoders in three nested NER datasets.

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Guanqing Liang, Cane Wing-Ki Leung

Modular Monolingual Adaptation using Pretrained Language Models (2026.acl-industry)

Challenge:	Named Entity Recognition (NER) is a fundamental building block for various downstream natural language processing tasks due to the ambiguous word boundaries and complex composition.
Approach:	They propose to resample entities within the same category to encourage a model to leverage both name and context knowledge in the training process.
Outcome:	The proposed method significantly improves a model’s ability to detect unseen entities, especially for company, organization and position categories.

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Nalin Kumar, Ondrej Dusek

A Neural Layered Model for Nested Named Entity Recognition (N18-1)

Challenge:	Existing approaches to building monolingual models for low-resource languages require a full model tuning process.
Approach:	They propose a modular approach to build monolingual models for low-resource languages by finetuning the whole model on the target language.
Outcome:	The proposed model improves on natural language understanding tasks on Scottish Gaelic, Irish, and Quechua with Quechuan being a very low-resource language.

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Meizhi Ju, Makoto Miwa, Sophia Ananiadou

DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)

Challenge:	Entity mentions embedded in longer entity mentions are referred to as nested entities due to the properties of natural language.
Approach:	They propose a neural model that dynamically stacks flat NER layers to identify nested entities by capturing sequential context representation with bidirectional long-term memory.
Outcome:	The proposed model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively in terms of F-score.

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, Yunbo Cao

A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER (2023.findings-emnlp)

Challenge:	Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese.
Approach:	They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition.
Outcome:	The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER).

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Yuhao Zhang, Yongliang Wang

DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition (2025.findings-naacl)

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing.
Approach:	They propose a query-parallel MRC-based approach to named entity recognition . the model is trained with parameter-efficient tuning technique, making it more data-efficient .
Outcome:	The proposed model performs competitively against strong baseline methods in resource-rich settings and achieves state-of-the-art results in low-resource settings.

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Qi Zhang, Huitong Pan, Zhijia Chen, Longin Jan Latecki, Cornelia Caragea, Eduard Dragut

UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus (2021.naacl-main)

Challenge:	Existing methods to identify entities using distant annotations are expensive and time-consuming.
Approach:	They propose a training dynamics-based label cleaning approach to characterize distant annotations and an automatic threshold estimation strategy to locate errors in distant labels.
Outcome:	The proposed method outperforms several advanced DS-NER approaches across four datasets.

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George Michalopoulos, Yuanxin Wang, Hussam Kaka, Helen Chen, Alexander Wong

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)

Challenge:	Contextual word embedding models do not take into account structured expert domain knowledge from a knowledge base.
Approach:	They propose a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy.
Outcome:	The proposed model outperforms existing domain-specific models on common named-entity recognition (NER) and clinical natural language inference tasks.

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Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao

Contextual String Embeddings for Sequence Labeling (C18-1)

Challenge:	Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well.
Approach:	They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data.
Outcome:	The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively.

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Alan Akbik, Duncan Blythe, Roland Vollgraf

Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)

Challenge:	Recent advances in language modeling have made it viable to model language as distributions over characters.
Approach:	They propose to leverage internal states of a trained character language model to produce a new type of word embeddings.
Outcome:	The proposed embeddings outperform the state-of-the-art on four classic sequence labeling tasks.

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Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, Samia Touileb

Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)

Challenge:	Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training.
Approach:	They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain.
Outcome:	The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model.

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Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data (2021.acl-long)

Challenge:	Named entity recognition (NER) is one of the fundamental tasks in natural language processing.
Approach:	They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner .
Outcome:	The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages.

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Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin, Tuo Zhao

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021.acl-long)

Challenge:	Existing work focuses on learning deep NER models with weak supervision without any human annotation.
Approach:	They propose a framework that can suppress the noise of the weak labels and fine-tune over the strongly labeled data.
Outcome:	The proposed framework outperforms existing methods on Named Entity Recognition tasks with weak supervision and weakly labeled data.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition (2022.findings-naacl)

Challenge:	Recent work shows document-level contexts can significantly improve Named Entity Recognition models.
Approach:	They propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine with the original sentence as the query.
Outcome:	The proposed approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.

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Yingjie Gu, Xiaoye Qu, Zhefeng Wang, Yi Zheng, Baoxing Huai, Nicholas Jing Yuan

MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)

Challenge:	Named entity recognition (NER) is a system for identifying text spans pertaining to specific entity types.
Approach:	They propose a method to investigate the regularity of Chinese NER's entity mentions by a regularity-aware module and a periodicity-gnostic module.
Outcome:	The proposed model significantly outperforms previous state-of-the-art methods on three benchmark datasets and a practical medical dataset.

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Shuhui Wu, Yongliang Shen, Zeqi Tan, Wenqi Ren, Jietian Guo, Shiliang Pu, Weiming Lu

Span-based Named Entity Recognition by Generating and Compressing Information (2023.eacl-main)

Challenge:	Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus.
Approach:	They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling .
Outcome:	The proposed network achieves state-of-the-art on several DS-NER benchmarks.

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Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou

A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)

Challenge:	Existing work on Named Entity Recognition (NER) only used generative or information compression models to improve performance.
Approach:	They propose to combine two types of IB models into one system to enhance Named Entity Recognition (NER) they incorporate unsupervised generative components span reconstruction and synonym generation into a span-based NER system.
Outcome:	The proposed model focuses on learning span representation, which is applicable not only to span-based NER but also to other span-related tasks such as event coreference resolution and question answering.

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Wenjie Xu, Yidan Chen, Jianquan Ouyang

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cecile Paris

Challenge:	Existing approaches to few-shot named entity recognition require large amounts of labeled data.
Approach:	They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures .
Outcome:	The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset.

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Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition (2022.lrec-1)

Challenge:	Existing studies on how to select appropriate data to pretrain word vectors or LMs are lacking.
Approach:	They propose to quantify aspects of similarity between pretraining and target data.
Outcome:	The proposed measures are good predictors of the usefulness of pretrained models for Named Entity Recognition over 30 data pairs.

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Elena V. Epure, Romain Hennequin

Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)

Challenge:	Existing studies have focused on auto-regressive models for generalization in named entity (NE) typing (NET) and recognition (NER) . however, little has been done in this direction for auto-Regressive LMs despite their popularity and potential to express a wide variety of NLP tasks in the same unified format.
Approach:	They propose to probe auto-regressive LMs for NET and NER generalization by resorting to meta-learning to assess the model's memorization of NEs.
Outcome:	The proposed model performs well on NET and NER generalization tasks, while relying more on NE than contextual cues in few-shot NER.

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Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Chang-Tien Lu

Attack Named Entity Recognition by Entity Boundary Interference (2024.lrec-main)

Challenge:	Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence.
Approach:	They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise.
Outcome:	The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages.

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Yifei Yang, Hongqiu Wu, Hai Zhao

Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)

Challenge:	Named Entity Recognition (NER) is a cornerstone natural language processing task . despite its robustness, studies on its robustity are lacking.
Approach:	They propose a one-word modification NER attack that strategically inserts a new boundary into the sentence and triggers the model to make a wrong recognition.
Outcome:	The proposed method is effective on English and Chinese models with 70%-90% success rate.

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Aaron Reich, Jiaao Chen, Aastha Agrawal, Yanzhe Zhang, Diyi Yang

Label Semantics for Few Shot Named Entity Recognition (2022.findings-acl)

Challenge:	Named Entity Recognition (NER) systems perform well on in-distribution data, but perform poorly on examples drawn from a shifted distribution.
Approach:	They propose to use expert-guided heuristics to change entity tokens and their contexts to alter their entity types as adversarial attacks.
Outcome:	The proposed model significantly improves performance on the challenging set and out-of-domain generalization.

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Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth

Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)

Challenge:	Named entity recognition (NER) is a fundamental natural language understanding task that requires large amounts of high quality annotated in-domain data.
Approach:	They propose a neural architecture that leverages the semantic information in the names of the labels to give the model additional signal and enriched priors.
Outcome:	The proposed model is especially effective in low resource settings.

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Xiaochen Wang, Yue Wang

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)

Challenge:	named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities .
Approach:	They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities .
Outcome:	The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora.

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Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu, Jing Yang

An Empirical Exploration of Local Ordering Pre-training for Structured Prediction (2020.findings-emnlp)

Challenge:	Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications.
Approach:	They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally.
Outcome:	The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance.

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Zhisong Zhang, Xiang Kong, Lori Levin, Eduard Hovy

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

Challenge:	Recent studies have shown that pre-training contextualized encoders with language model objectives is effective for structured prediction.
Approach:	They propose a semi-supervised method for pre-training contextualized encoders with language model objectives.
Outcome:	The proposed method is effective on three typical structured prediction tasks in four languages.

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Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang

Template-Based Named Entity Recognition Using BART (2021.findings-acl)

Challenge:	Named Entity Recognition is a key task whose performance is sensitive to genre and language.
Approach:	They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages.
Outcome:	The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains.

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Leyang Cui, Yu Wu, Jian Liu, Sen Yang, Yue Zhang

Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023.eacl-main)

Challenge:	Existing methods for fewshot NER do not make full use of knowledge transfer in NER model parameters.
Approach:	They propose a template-based method for NER that treats NER as a language model ranking problem in a sequence-to-sequence framework.
Outcome:	The proposed method achieves 92.55% F1 score on the CoNLL03 task and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 scores on the MIT Movie, the ATIS, and the MATLAB task.

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Nicolas Hiebel, Olivier Ferret, Karen Fort, Aurélie Névéol

A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)

Challenge:	In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets.
Approach:	They use auto-regressive neural models to generate a clinical case corpus annotated with clinical entities and evaluate it for a named entity recognition task.
Outcome:	The proposed model can produce clinical case corpus annotated with clinical entities while maintaining confidentiality.

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Jinpeng Hu, He Zhao, Dan Guo, Xiang Wan, Tsung-Hui Chang

Dynamic Prefix as Instructor for Incremental Named Entity Recognition: A Unified Seq2Seq Generation Framework (2025.findings-acl)

Challenge:	Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored.
Approach:	They propose a framework that borrows entity information from the source domain to enhance NER in the target domain.
Outcome:	The proposed model improves over the state-of-the-art model on several datasets.

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Zihao Wu, YongXiang Hua, Yongxin Zhu, Fang Zhang, Linli Xu

Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags (C18-1)

Challenge:	Named Entity Recognition (NER) is a fundamental problem in information extraction.
Approach:	They propose a parameter-efficient method for Incremental Named Entity Recognition (INER) task aimed at updating a model to extract entities from an expanding set of entity type candidates by employing a dynamic prefix as a task instructor to guide the generative model.
Outcome:	Empirical results show that the proposed method preserves task-invariant knowledge while adapting to new entities with minimal parameter updates.

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Onur Güngör, Suzan Uskudarli, Tunga Güngör

Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)

Challenge:	Named entity recognition (NER) taggers require external morphological disambiguation tools to function which are hard to obtain or non-existent for many languages.
Approach:	They propose a model which jointly learns NER and MD taggers in languages for which one can provide a list of candidate morphological analyses.
Outcome:	The proposed model can be trained independently of the morphological annotation schemes, and it performs competitively.

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Jonas Golde, Felix Hamborg, Alan Akbik

Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)

Challenge:	Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text.
Approach:	They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition.
Outcome:	The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations.

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Yu Xia, Quan Wang, Yajuan Lyu, Yong Zhu, Wenhao Wu, Sujian Li, Dai Dai

A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)

Challenge:	Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved.
Approach:	They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion.
Outcome:	The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0.

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Vikas Yadav, Steven Bethard

Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)

Challenge:	Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc.
Approach:	They propose to use recurrent neural networks to generate NERs over characters, sub-words and/or word embeddings to improve named entity recognition.
Outcome:	The proposed architectures are better than those based on feature engineering and other supervised or semi-supervised learning algorithms.

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Yaosheng Yang, Wenliang Chen, Zhenghua Li, Zhengqiu He, Min Zhang

Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)

Challenge:	Existing approaches to named entity recognition (NER) in Chinese are limited by the lack of annotated data.
Approach:	They propose a method which can automatically populate annotated training data without humancost by using distant supervision.
Outcome:	The proposed method performs better than comparison systems on two datasets.

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Matias Rojas, Felipe Bravo-Marquez, Jocelyn Dunstan

A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)

Challenge:	Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories.
Approach:	They propose to revisit the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type.
Outcome:	The proposed model achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models.

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Yanis Labrak, Mickael Rouvier, Richard Dufour

BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored .
Approach:	They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English.
Outcome:	The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English.

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Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Dong-Kyu Chae

Structured Data in NLP

Challenge:	Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers.
Approach:	Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text.
Outcome:	The proposed framework can automatically construct Bengali KGs from any Bangla text.

Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)

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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren

Retrieval Augmented Instruction Tuning for Open NER with Large Language Models (2025.coling-main)

Challenge:	Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates.
Approach:	They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning.
Outcome:	The proposed method improves on in-domain learning and domain adaptation in low-resource settings.

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Tingyu Xie, Jian Zhang, Yan Zhang, Yuanyuan Liang, Qi Li, Hongwei Wang

Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition (2022.naacl-main)

Challenge:	Existing studies have focused on integrating large language models (LLMs) with information extraction (IE) however, the best approach to incorporate information with LLMs for IE remains an open question.
Approach:	They propose to use a Chinese IT dataset to perform RA-IT for IE . they use semantically similar examples from the training dataset as the context .
Outcome:	The proposed approach is evaluated in English and Chinese scenarios.

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Besnik Fetahu, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi

SetGNER: General Named Entity Recognition as Entity Set Generation (2022.emnlp-main)

Challenge:	Named entity recognition (NER) models trained on CoNLL do not transfer well to other domains, even within the same language.
Approach:	They propose a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain.
Outcome:	The proposed model improves on cross-lingual transfer with an F1 score of 92.92 for English and an average of 89.43 across all languages in CoNLL.

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Yuxin He, Buzhou Tang

Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition (2022.findings-naacl)

Challenge:	Named entity recognition (NER) is a fundamental task in the field of information extraction and has played an important role in the development of natural language processing.
Approach:	They propose a method that treats each entity as a sequence and is capable of recognizing discontinuous mentions.
Outcome:	The proposed model outperforms state-of-the-art generative NER models on two discontinuous NER datasets, two nested NER and one flat NER.

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Huaiyuan Ying, Shengxuan Luo, Tiantian Dang, Sheng Yu

Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (2023.findings-acl)

Challenge:	Existing methods to locate and classify entities using knowledge bases and unlabeled corpus are expensive and limited application.
Approach:	They propose to use a method to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing them in contrastive learning frameworks.
Outcome:	The proposed method can give modified suggestions on distant data without additional supervised labels and thus reduces the requirement on the quality of the knowledge bases.

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Shan Zhang, Bin Cao, Tianming Zhang, Yuqi Liu, Jing Fan

NerKor+Cars-OntoNotes++ (2022.lrec-1)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore label dependency, resulting in suboptimal performance.
Approach:	They propose a meta-learning method to make label dependency transferable by learning general initialization and individual parameter update rule for label dependency.
Outcome:	The proposed method improves existing methods by learning general initialization and individual parameter update rule for label dependency.

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Attila Novák, Borbála Novák

Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)

Challenge:	In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus . it contains twice as many annotated spans and 7 times as many distinct entity types as the original version.
Approach:	They present an upgraded version of the Hungarian NYTK-NerKor named entity corpus with an extended OntoNotes 5 annotation scheme.
Outcome:	The enhanced version of the corpus contains twice as many annotated spans and 7 times more distinct entity types than the original version.

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Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang

HardEval: Focusing on Challenging Tokens to Assess Robustness of NER (2020.lrec-1)

Challenge:	Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles.
Approach:	They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data .
Outcome:	Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases .

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Gabriel Bernier-Colborne, Phillippe Langlais

A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)

Challenge:	Named entity recognition (NER) systems are often evaluated on human annotations . a new evaluation method focuses on subsets of tokens that represent specific sources of errors .
Approach:	They propose a method that focuses on subsets of tokens that represent specific sources of errors.
Outcome:	The proposed evaluation method focuses on subsets of tokens that represent specific sources of errors.

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Toqeer Ehsan, Thamar Solorio

WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (2021.findings-emnlp)

Challenge:	Existing NER benchmarks lack quality annotations, resulting in poor performance.
Approach:	They propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence.
Outcome:	The proposed approach improves NER performance on three datasets with a high number of missing annotations.

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Simone Tedeschi, Valentino Maiorca, Niccolò Campolungo, Francesco Cecconi, Roberto Navigli

Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing (2022.coling-1)

Challenge:	Named Entity Recognition (NER) is a key intermediate task in NLP.
Approach:	They propose a method which uses knowledge-based approaches and neural models to produce high-quality training corpora for NER.
Outcome:	The proposed method improves on standard benchmarks and yields significant improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.

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Yifei Yang, Zuchao Li, Hai Zhao

NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)

Challenge:	Named entity recognition is a natural language processing task . nested NER is based on a linear structure, but there is no research on applying corpus-level information to NER.
Approach:	They propose a holistic structure parsing algorithm to reveal the entire NEs in a sentence . they introduce points-wise mutual information and other frequency features from corpus-aware statistics .
Outcome:	The proposed model outperforms existing models on widely-used benchmarks and achieves state-of-the-art.

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Uri Katz, Matan Vetzler, Amir Cohen, Yoav Goldberg

A Large-Scale Chinese Multimodal NER Dataset with Speech Clues (2021.acl-long)

Challenge:	Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them .
Approach:	They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition .
Outcome:	The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs .

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Dianbo Sui, Zhengkun Tian, Yubo Chen, Kang Liu, Jun Zhao

COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)

Challenge:	Using a large-scale dataset, we explore Chinese named entity recognition (NER) with both textual and acoustic contents.
Approach:	They propose a Chinese multimodal named entity recognition dataset . their corpus contains 42,987 annotated sentences and 71 hours of speech data .
Outcome:	The proposed model yields state-of-the-art (SoTA) results on Chinese multimodal named entity recognition (NER) based on 42,987 annotated sentences and 71 hours of speech data.

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Yucheng Huang, Kai He, Yige Wang, Xianli Zhang, Tieliang Gong, Rui Mao, Chen Li

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking (2022.findings-acl)

Challenge:	Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity.
Approach:	They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class.
Outcome:	The proposed approach outperforms state-of-the-art models with a significant margin in most cases.

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Yifei Li, Pratheeksha Nair, Kellin Pelrine, Reihaneh Rabbany

ReCoT-NER: Enhancing Zero-Shot Named Entity Recognition through Chain-of-Thought Prompting and Recall-Oriented Loss Optimization (2026.findings-acl)

Challenge:	Existing methods for Named-Entity Recognition (NER) on escort ads are not sufficient to extract person names from the text of the ad.
Approach:	They propose to use a model to extract person names from escort ads to capture ambiguous names and adapt to adversarial changes in the text.
Outcome:	The proposed model shows 19% improvement on average in the F1 classification score compared to previous state-of-the-art in two domain-specific datasets.

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Dabin Fu, Fanghong Zhang

SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition (2022.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a key component of natural language processing (NLP) but it is difficult to implement in specialized domains such as wind power fault diagnosis.
Approach:	They propose a reasoning-enhanced generative framework that integrates Chain-of-Thought prompting and recall-oriented loss optimization to address these challenges.
Outcome:	The proposed framework improves recall and overall F1 performance across general and industrial domains.

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Jianing Wang, Chengyu Wang, Chuanqi Tan, Minghui Qiu, Songfang Huang, Jun Huang, Ming Gao

AHVE-CNER: Aligned Hanzi Visual Encoding Enhance Chinese Named Entity Recognition with Multi-Information (2025.coling-main)

Challenge:	Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification.
Approach:	They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning .
Outcome:	The proposed model outperforms strong baselines over multiple benchmarks.

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Xuhui Zheng, Zhiyuan Min, Bin Shi, Hao Wang

Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition (2022.findings-emnlp)

Challenge:	Existing glyph-based models neglect the relationship between pictorial elements and radicals for Named Entity Recognition (NER) tasks.
Approach:	They propose a model that integrates multi-source visual and phonetic information of Hanzi . they propose combining pictographic features with radicals to facilitate integration .
Outcome:	The proposed model improves performance on benchmark datasets.

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Zihan Wang, Kewen Zhao, Zilong Wang, Jingbo Shang

Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark (2024.naacl-long)

Challenge:	Existing methods for fine-tuning pre-trained language models are limited . we propose a few-shot fine-uning framework for NER .
Approach:	They propose a few-shot fine-tuning framework for named entity recognition (NER) they propose three new types of tokens, "is-entity", "which-type" and "bracket"
Outcome:	The proposed framework improves on pre-trained language models on several benchmark datasets.

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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Suppa, Hila Gonen, Joseph Marvin Imperial, Börje Karlsson, Peiqin Lin, Nikola Ljubešić, Lester James Miranda, Barbara Plank, Arij Riabi, Yuval Pinter

A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models (2024.naacl-long)

Challenge:	In named entity recognition, the majority of annotation efforts are centered on English, and cross-lingual transfer performance remains brittle.
Approach:	They propose to develop gold-standard named entity recognition benchmarks in many languages using a cross-lingual consistent schema.
Outcome:	The proposed benchmarks will be released to the public in 2022 . they will provide baselines on in-language and cross-lingual learning settings.

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Xingmeng Zhao, Ali Niazi, Anthony Rios

Semantic Annotation for Improved Safety in Construction Work (2020.lrec-1)

Challenge:	Chemical named entity recognition (NER) models are used in many downstream tasks, but it is unknown whether they work the same for everyone.
Approach:	They develop a framework for measuring gender bias in chemical NER models . they analyze a corpus of 92,405 words with self-identified gender information from reddit .
Outcome:	The proposed framework measures gender bias in chemical NER models using synthetic data and a newly annotated corpus of over 92,405 words with self-identified gender information from Reddit.

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Paul Thompson, Tim Yates, Emrah Inan, Sophia Ananiadou

An Encoding Strategy Based Word-Character LSTM for Chinese NER (N19-1)

Challenge:	a number of documents provide evidence of previous incidents and mitigation strategies . but information about previous projects with similar attributes is often hidden within . a new named entity annotation scheme is being developed for construction safety .
Approach:	a team of four health and safety experts have developed a named entity annotation scheme for construction safety documents.
Outcome:	a new named entity annotation scheme annotates 600 sentences from accident reports . the scheme has an average agreement rate of 0.79 F-Score .

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Wei Liu, Tongge Xu, Qinghua Xu, Jiayu Song, Yueran Zu

Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)

Challenge:	Existing word-based model can not be trained in batches due to its DAG structure.
Approach:	They propose a lattice model that integrates word information into the start or end characters of a word and integrates it into a fixed-sized representation for efficient batch training.
Outcome:	The proposed model outperforms other state-of-the-art models on benchmark datasets and shows that it can be trained in batches without a shortcut path.

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Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, Zhiyuan Liu

Coarse-to-fine Few-shot Learning for Named Entity Recognition (2023.findings-acl)

Challenge:	Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded.
Approach:	They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models .
Outcome:	The proposed model is the first few-shot NER dataset and the largest human-crafted NER data set.

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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen

2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)

Challenge:	Existing few-shot NER solutions do not consider sub-class discrimination and various granularity of new classes during coarse training.
Approach:	They propose a method that uses a cluster-based prototype loss to learn group-wise discriminative representations of coarse-grained classes and a mixture prototype loss for learning the representations.
Outcome:	The proposed method shows superior performance over baseline methods on in-domain and cross-domain settings with various target granularity.

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Jiasheng Zhang, Xikai Liu, Xinyi Lai, Yan Gao, Shusen Wang, Yao Hu, Yiqing Lin

CHisIEC: An Information Extraction Corpus for Ancient Chinese History (2024.lrec-main)

Challenge:	Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP).
Approach:	They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence.
Outcome:	The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms.

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Xuemei Tang, Qi Su, Jun Wang, Zekun Deng

Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City (2023.emnlp-main)

Challenge:	Historical and cultural heritage preservation is an important branch of digital humanities, where the rich tapestry of the past meets the cutting-edge tools of the digital age.
Approach:	They present a dataset to evaluate NER and RE tasks in ancient Chinese history . they use four distinct entity types and twelve relation types to identify them .
Outcome:	The "Chinese Historical Information Extraction Corpus" is a dataset from 13 dynasties spanning over 1830 years . the dataset encompasses four distinct entity types and twelve relation types .

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Mikael Brunila, Jack LaViolette, Sky CH-Wang, Priyanka Verma, Clara Féré, Grant McKenzie

GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer (2024.naacl-long)

Challenge:	Critical toponymy studies the dynamics of power, capital, and resistance through place names and the sites to which they refer.
Approach:	They propose a model that measures how cultural and economic capital shape the ways in which people refer to places through an annotated dataset of Airbnb listings in New York City.
Outcome:	The proposed model can identify important discourse categories integral to the characterization of place.

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Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois

RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models (2021.emnlp-main)

Challenge:	Named Entity Recognition (NER) models are limited to a set of predefined entity types. Large language models (LLMs) can extract arbitrary entities through natural language instructions.
Approach:	They propose a model that can identify any type of entity using a transformer encoder.
Outcome:	The proposed model outperforms existing models on NER benchmarks on a set of predefined entities.

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Bill Yuchen Lin, Wenyang Gao, Jun Yan, Ryan Moreno, Xiang Ren

Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (D18-1)

Challenge:	Recent named entity recognition models have great performance on many conventional benchmarks, but it is not reliable in realistic applications.
Approach:	They propose a method to create natural adversarial examples using Wikidata and pre-trained language models.
Outcome:	The proposed method produces natural adversarial examples with a shifted distribution from training data.

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Nathan Greenberg, Trapit Bansal, Patrick Verga, Andrew McCallum

Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)

Challenge:	Existing large labeled text datasets contain labels for multiple subsets of biomedical entity types, but it is rare to find large labeling datasets containing all desired entity types together.
Approach:	They propose a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types.
Outcome:	The proposed method improves NER F1 over training in isolation on biocreative V CDR, biocreativ VI ChemProt and MedMentions datasets.

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Mohammad Golam Sohrab, Makoto Miwa

Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction (2024.findings-acl)

Challenge:	Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text.
Approach:	They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions .
Outcome:	The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources.

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Yu Yang, Jinyu Guo, Kai Shuang, Chenrui Mao

CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)

Challenge:	Existing methods for incorporating entities into EAE rely on prompts or NER . weak semantic associations due to missing role-entity correspondence cues . one-sided semantic understanding relying solely on argument role semantics a problem .
Approach:	They propose an EAE model with stage-customized entity type embedding to explore the role of entity types.
Outcome:	The proposed model achieves state-of-the-art performance on mainstream benchmarks and robustness in low-resource settings.

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Xiang Wei, Yufeng Chen, Ning Cheng, Xingyu Cui, Jinan Xu, Wenjuan Han

Evaluating the Utility of Hand-crafted Features in Sequence Labelling (D18-1)

Challenge:	Existing IE tools lack multi-task support and automatic updates for KG and EKG construction.
Approach:	They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine.
Outcome:	The proposed tool improves annotation quality, efficiency, and stability simultaneously.

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Minghao Wu, Fei Liu, Trevor Cohn

Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)

Challenge:	Conventional wisdom is that hand-crafted features are redundant for deep learning models . authors propose a method for using handcrafted features in a hybrid learning approach .
Approach:	They propose a method for exploiting handcrafted features as part of a hybrid learning approach.
Outcome:	The proposed method outperforms baseline models on a named entity recognition task and reduces training requirements to 60% while maintaining the same predictive accuracy.

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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang

Contextual Label Projection for Cross-Lingual Structured Prediction (2024.naacl-long)

Challenge:	Existing approaches to learn a model from labeled data are expensive or prohibitive.
Approach:	They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain.
Outcome:	The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin.

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Tanmay Parekh, I-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang, Nanyun Peng

A Data-driven Approach to Named Entity Recognition for Early Modern French (2022.coling-1)

Challenge:	Prior work favors simplified label translation or relying on word-level alignments for label projection.
Approach:	They propose a novel approach CLaP which translates text to target language and performs contextual translation on the labels using the translated text as the context.
Outcome:	The proposed approach improves translation accuracy on two prediction tasks and shows 2.4 F1 improvement for EAE and 1.4 F1 for named entity recognition.

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Pedro Ortiz Suarez, Simon Gabay

CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English (2024.lrec-main)

Challenge:	Named entity recognition is an important task in natural language processing.
Approach:	They propose to use a data-driven approach to identify historical French with fine-grained annotations instead of a specialised architecture to tackle particularities.
Outcome:	The proposed corpus is larger than the most popular NER evaluation corpora for both Contemporary English and French.

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Andrew Rueda, Elena Alvarez-Mellado, Constantine Lignos

Explainability & Interpretability in NLP

Challenge:	a glass ceiling for named entity recognition systems has been suggested for 2021 . however, the performance of the most popular NER benchmarks has plateaued since then . we investigate what NER models are still struggling with .
Approach:	They perform a fine-grained evaluation of the model outputs by adding document annotations to the CoNLL-03 English dataset to identify lingering errors.
Outcome:	The proposed model is able to correct errors and guide future work.

Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature (2022.coling-1)

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Sajawel Ahmed, Rob van der Goot, Misbahur Rehman, Carl Kruse, Ömer Özsoy, Alexander Mehler, Gemma Roig

Attending to Long-Distance Document Context for Sequence Labeling (2020.findings-emnlp)

Challenge:	Named entity recognition and topic modeling are crucial for downstream tasks in natural language processing.
Approach:	They propose to address named entity recognition and topic modeling on CA literature . they manually annotate the work of Tafsir Al-Tabari with span-based NEs .
Outcome:	The results show that the proposed task can perform state-of-the-art on historical topic models.

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Matthew Jörke, Jon Gillick, Matthew Sims, David Bamman

MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)

Challenge:	UC Berkeley researchers develop a method for incorporating global context in long documents . many of the main datasets used in NLP are comprised of relatively short documents - english OntoNotes contains 223 tokens .
Approach:	They propose a method for incorporating global context in long documents . they use multiple mentions of the same word type to generate a representation for each token .
Outcome:	The proposed model performs better at recognizing entities with high TF-IDF scores than parametric models lacking context.

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Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko

Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (P19-1)

Challenge:	Named Entity Recognition (NER) is a core task in Natural Language Processing.
Approach:	They present a large multilingual dataset for Named Entity Recognition that covers 3 domains across 11 languages and multilingual and code-mixing subsets.
Outcome:	The proposed dataset is large and multilingual, covering 11 languages and subsets.

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Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok

Packed Levitated Marker for Entity and Relation Extraction (2022.acl-long)

Challenge:	Named entity recognition (NER) is an important step in most natural language processing (NLP) applications.
Approach:	They propose a dual-adversarial neural transfer method for addressing low-resource Named Entity Recognition (NER) they propose 'Generalized Resource-Adversarial Discriminator' and 'accidental training'
Outcome:	The proposed method improves on low-resource Named Entity Recognition (NER) with two variants, i.e., DATNet-F and DATNET-P, and adversarial training is adopted to boost model generalization.

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Deming Ye, Yankai Lin, Peng Li, Maosong Sun

CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)

Challenge:	Existing work on entity and relation extraction ignores the interrelation between spans . a novel approach to extract better span representations from pre-trained languages is needed .
Approach:	They propose a span representation approach that packs Levitated Markers to consider interrelation between spans.
Outcome:	The proposed model improves on baselines on six NER benchmarks and achieves a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models.

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Yuying Zhu, Guoxin Wang

On the Strength of Character Language Models for Multilingual Named Entity Recognition (D18-1)

Challenge:	Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters.
Approach:	They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts.
Outcome:	The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets.

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Xiaodong Yu, Stephen Mayhew, Mark Sammons, Dan Roth

EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Entity Recognition with Large Language Models (2025.coling-main)

Challenge:	Character-level patterns have been widely used in English Named Entity Recognition systems.
Approach:	They propose to use corpus-agnostic character-level language models to capture name tokens . they demonstrate they can capture name and non-name tokens in a diverse set of languages .
Outcome:	The proposed model improves the performance of an off-the-shelf NER system for multiple languages.

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Zeliang Tong, Zhuojun Ding, Wei Wei

Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data.
Approach:	They propose an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement.
Outcome:	The proposed framework shows consistent performance improvements on four benchmarks.

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Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu, Zhenhua Ling, Wu Guo, Quan Liu, Zhigang Chen, Cong Liu

ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER (2023.findings-acl)

Challenge:	Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer.
Approach:	They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently.
Outcome:	The proposed method shows great generalization and compatibility across languages and fields.

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Karan Aggarwal, Henry Jin, Aitzaz Ahmad

Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance (2024.lrec-main)

Challenge:	Named Entity Recognition (NER) requires high-quality labeled datasets.
Approach:	They propose a method that uses pre-trained language models to generate entity-controlled text to augment small labeled datasets for downstream NER tasks.
Outcome:	The proposed method produces full text samples with desired entities appearing in a controllable way while retaining sentence coherence closest to the real world data.

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Cheril Shah, Yashashree Chandak, Atharv Mahesh Mane, Benjamin Bergen, Tyler A. Chang

SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)

Challenge:	Pre-trained multilingual language models represent multiple languages in a single vector space, a feature hypothesized to enable impressive crosslingual transfer capabilities.
Approach:	They propose to use a multilingual representation space that sorts axes based on their language-separability to determine whether geometric distances between languages correlate with crosslingual transfer performance.
Outcome:	The proposed measures do not generalize well across models, layers, and tasks.

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Wazir Ali, Junyu Lu, Zenglin Xu

From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers (2020.emnlp-main)

Challenge:	Named entity recognition is an essential lower-level task in natural language processing (NLP).
Approach:	They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines.
Outcome:	The proposed dataset is likely to be a significant resource for statistical Sindhi language processing.

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Anne Lauscher, Vinit Ravishankar, Ivan Vulić, Goran Glavaš

Happiness is Sharing a Vocabulary: A Study of Transliteration Methods (2026.eacl-long)

Challenge:	Existing studies show that multilingual transformers are less effective in resource-lean scenarios and for distant languages.
Approach:	They propose to use massively multilingual transformers to pretrain languages . they show that MMTs are less effective in resource-lean scenarios and distant languages if they are pre-trained via language modeling .
Outcome:	The proposed model is less effective in resource-lean scenarios and for distant languages than cross-lingual word embeddings.

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Haeji Jung, Jinju Kim, Kyungjin Kim, Youjeong Roh, David R. Mortensen

Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations (D18-1)

Challenge:	a key problem in multilingual NLP is script barrier, which makes it difficult to share knowledge between languages . a new study shows that transliteration can be useful for languages using non-Latin scripts .
Approach:	They propose to use romanization, phonemic transcription, and substitution ciphers to evaluate models . romanization outperforms other input types in 7 out of 8 evaluation settings .
Outcome:	The proposed approach outperforms other input types on three tasks and is the most effective . romanization outperformed other input type in 7 out of 8 evaluation settings .

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Aditi Chaudhary, Chunting Zhou, Lori Levin, Graham Neubig, David R. Mortensen, Jaime Carbonell

De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention (2021.acl-long)

Challenge:	Existing approaches to generalization to resource-rich languages are difficult . a recent study shows that word representations can be useful in low resource languages .
Approach:	They propose two approaches for improving generalization to low-resource languages by adapting continuous word representations using linguistically motivated subword units.
Outcome:	The proposed method improves generalization to low resource languages . it requires neither parallel corpora nor bilingual dictionaries and requires no parallel training .

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Wenkai Zhang, Hongyu Lin, Xianpei Han, Le Sun

The Bulgarian Event Corpus: Overview and Initial NER Experiments (2022.lrec-1)

Challenge:	Existing methods for Named entity recognition (NER) rely on labeled data, which is labor-intensive.
Approach:	They propose a method to de-biase DS-NER models by a structural Causal Model . they propose to use a causal invariance regularizer to make them more robust .
Outcome:	The proposed method significantly improves DS-NER models on four datasets and three DS NER models.

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Petya Osenova, Kiril Simov, Iva Marinova, Melania Berbatova

Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models (2024.lrec-main)

Challenge:	Initial experiments on standard NER task due to complexity of dataset and rich NE annotation scheme are promising with respect to some labels and give insights on handling better other ones.
Approach:	They describe a Bulgarian Event Corpus (BEC) that includes named entities and events with their roles.
Outcome:	The proposed corpus is multi-domain and oriented towards Social Sciences and Humanities (SSH) it includes named entities and events with their roles.

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Ana Cimitan, Ana Alves Pinto, Michaela Geierhos

Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)

Challenge:	Named Entity Recognition (NER) models are susceptible to gender bias . benchmark datasets are curated specifically for a given NLP task .
Approach:	They propose to filter out benchmark templates with a higher probability of detecting gender bias in NER models.
Outcome:	The proposed method is based on masked token prediction and tested in English and german using the corresponding fine-tuned BERT base model.

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Pan Yang, Xin Cong, Zhenyu Sun, Xingwu Liu

Representation Learning

Challenge:	Existing approaches to extract text spans from plain text do not fully exploit label knowledge.
Approach:	They propose a model to integrate label knowledge into text representations by encoding texts and annotations independently and then integrating label knowledge with an elaborate-designed semantics fusion module.
Outcome:	The proposed model achieves state-of-the-art performance on four benchmarks and reduces training time and inference time by 76% and 77% on average compared with the existing paradigm.

Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition (2025.findings-acl)

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Nagham Hamad, Mohammed Khalilia, Mustafa Jarrar

Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (2022.coling-1)

Challenge:	Using the Wojood framework, we compare existing Arabic Named Entity Recognition models with domain and dialect divergence and resource scarcity.
Approach:	They propose a multi-dimensional Arabic named entity corpus covering 16 dialects across 10 domains and an annotation scheme using the Wojood guidelines.
Outcome:	The proposed model performs better on 16 dialects across 10 domains and 16 domains, while other models struggle with different dialects and domains.

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Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, Dietrich Klakow

MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (2022.acl-long)

Challenge:	Multilingual pre-trained language models have shown impressive performance on several downstream tasks for both high-resourced and low-resource languages.
Approach:	They propose to apply multilingual adaptive fine-tuning to 17 most-resourced African languages and three other high-resource languages to encourage cross-lingual transfer learning.
Outcome:	The proposed approach is competitive to LAFT on individual languages while requiring significantly less disk space.

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Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang

A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)

Challenge:	Named Entity Recognition models are feature-engineering and machine learning based.
Approach:	They propose a new NER learning framework that uses entity mentions to improve model performance.
Outcome:	The proposed model achieves better performance on OOV entities on various settings and datasets.

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Uyen Phan, Phuong N.V Nguyen, Nhung Nguyen

Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations (2023.acl-long)

Challenge:	Named Entity Recognition (NER) is an important task in information extraction.
Approach:	They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure.
Outcome:	The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country .

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Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jaewoo Kang

Representation Learning

Challenge:	Named entity recognition models rely on domain-specific dictionaries provided by experts . however, such dictionary sets are infeasible in many domains where they do not exist .
Approach:	They propose a framework that generates NER datasets with high-coverage pseudo-dictionaries . phrase retrieval models are used to retrieve popular entities rather than rare ones .
Outcome:	The proposed framework outperforms the previous best model by an average F1 score of 4.7 across five NER benchmark datasets.

Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting (2024.findings-emnlp)

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Marco Naguib, Xavier Tannier, Aurélie Névéol

Comparing Annotated Datasets for Named Entity Recognition in English Literature (2022.lrec-1)

Challenge:	Named Entity Recognition (NER) is a critical task in information extraction that is not covered in recent benchmarks.
Approach:	They compare 13 auto-regressive models using prompting and 16 masked models using fine-tuning on 14 NER datasets covering English, French and Spanish.
Outcome:	The proposed models outperform auto-regressive models in English, French and Spanish on 14 NER datasets.

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Rositsa Ivanova, Marieke van Erp, Sabrina Kirrane

Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)

Challenge:	Generally speaking, the majority of NER tools struggle to perform well when the entities in the text contain specific characteristics.
Approach:	They analysed two existing annotated datasets and two additional gold standard datasets to evaluate the performance of two NER tools.
Outcome:	The results show that the performance of two NER tools varies significantly depending on the gold standard used for the individual evaluations.

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Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler

Large Sequence Representation Learning via Multi-Stage Latent Transformers (2022.coling-1)

Challenge:	Named entity recognition is a technique for extracting information from unstructured data with known labels.
Approach:	They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases .
Outcome:	The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods .

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Ionut-Catalin Sandu, Daniel Voinea, Alin-Ionut Popa

SKD-NER: Continual Named Entity Recognition via Span-based Knowledge Distillation with Reinforcement Learning (2023.emnlp-main)

Challenge:	a novel algorithm for named-entity recognition (NER) uses language and spatial features to predict entity tags for structured text . a dataset of 11,926 images depicting food product labels is used to perform NER tasks .
Approach:	They propose a multi-stage transformer architecture for named-entity recognition . they propose RADAR, an LSTM classifier operating at character level, to refine NER predictions .
Outcome:	The proposed method outperforms two competing models on a food label dataset.

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Yi Chen, Liang He

Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)

Challenge:	Continual learning for named entity recognition (CL-NER) aims to enable models to continuously learn new entity types while retaining the ability to recognize previously learned ones.
Approach:	They propose a model that leverages knowledge distillation to retain memory and employs reinforcement learning strategies to optimize the soft labeling and distillation losses generated by the teacher model to effectively prevent catastrophic forgetting.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it significantly improves the performance of the CL-NER task.

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Huiming Wang, Liying Cheng, Wenxuan Zhang, De Wen Soh, Lidong Bing

COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing (2025.findings-emnlp)

Challenge:	Existing DA methods for named entity recognition (NER) are costly and labor-intensive to acquire, necessitating innovative approaches to data scarcity.
Approach:	They propose an order-agnostic data augmentation solution that exploits the order-based property in the training phase of sequence-to-sequence NER methods for data augmented.
Outcome:	The proposed method significantly enhances the few-shot capabilities of pre-trained language models in low-resource settings.

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Rajvee Sheth, Himanshu Beniwal, Mayank Singh

Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)

Challenge:	COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset . 125K+ high-quality instances across five core NLP tasks are annotating by three bilingual annotators .
Approach:	COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset . 125K+ high-quality instances are annotating by three bilingual annotators .
Outcome:	The dataset covers five core NLP tasks, including Token-level Language Identification, Matrix Language Identification and Named Entity Recognition.

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Xiusheng Huang, Yubo Chen, Shun Wu, Jun Zhao, Yuantao Xie, Weijian Sun

ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision (2021.emnlp-main)

Challenge:	Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples.
Approach:	They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term .
Outcome:	The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets.

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Xuan Wang, Vivian Hu, Xiangchen Song, Shweta Garg, Jinfeng Xiao, Jiawei Han

SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities (2024.naacl-long)

Challenge:	Named entity recognition (NER) is a fundamental step in scientific literature analysis to build AI-driven systems for molecular discovery, synthetic strategy designing, and manufacturing.
Approach:	They propose an ontology-guided method for fine-grained named entity recognition (NER) it leverages the chemistry type ontologies to generate distant labels with flexible KB-matching .
Outcome:	The proposed method significantly outperforms the state-of-the-art methods with a .25 absolute F1 improvement.

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Hyunjong Ok, Taeho Kil, Sukmin Seo, Jaeho Lee

Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)

Challenge:	Named entity recognition (NER) is a task to identify textual spans that correspond to named entities in the given text.
Approach:	They propose a model that can generalize to entities unseen during training and handle noisy annotations.
Outcome:	The proposed model outperforms existing methods on both MNER and GMNER benchmarks and is robust and accurate.

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Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie

Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

Challenge:	Experimental results show that crowdsourced annotations are highly effective under supervised conditions.
Approach:	They propose an annotator-aware representation learning model that is inspired by domain adaptation methods which attempt to capture effective domain-alike features.
Outcome:	The proposed model is highly effective on a benchmark dataset and achieves state-of-the-art performance with only a very small scale of expert annotations.

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Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu

Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)

Challenge:	Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach:	They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome:	The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

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Xinyu Wang, Jiong Cai, Yong Jiang, Pengjun Xie, Kewei Tu, Wei Lu

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)

Challenge:	Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant.
Approach:	They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction.
Outcome:	The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset.

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Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu

Code and Named Entity Recognition in StackOverflow (2020.acl-main)

Challenge:	Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance.
Approach:	They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.
Outcome:	The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.

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Jeniya Tabassum, Mounica Maddela, Wei Xu, Alan Ritter

Type Enhanced BERT for Correcting NER Errors (2023.findings-acl)

Challenge:	StackOverflow has 15 million programming related questions written by 8.5 million users . however, there is a lack of fundamental NLP resources and techniques for identifying software-related named entities within natural language sentences.
Approach:	They propose a named entity recognition corpus for the computer programming domain with 15,372 sentences annotated with 20 fine-grained entity types.
Outcome:	The proposed model improves on 152 million sentences from StackOverflow and achieves 79.10 F-1 score for code and named entity recognition.

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Kuai Li, Chen Chen, Tao Yang, Tianming Du, Peijie Yu, Dong Du, Feng Zhang

A Unified Generative Framework for Various NER Subtasks (2021.acl-long)

Challenge:	Named entity recognition (NER) is the task of identifying spans that belong to particular categories, such as person, location, organization, etc.
Approach:	They propose a method that integrates named entity’s type information into BERT by an adapter layer and integrates it into a gazetteer.
Outcome:	The proposed method outperforms baselines in multiple corpus.

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Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, Xipeng Qiu

Prompt-Based Metric Learning for Few-Shot NER (2023.findings-acl)

Challenge:	Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences.
Approach:	They propose to formulate NER subtasks as entity span sequence generation task . framework can be used to solve all three kinds of NER tasks without tagging schema .
Outcome:	The proposed framework achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets.

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Yanru Chen, Yanan Zheng, Zhilin Yang

MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER (2021.acl-long)

Challenge:	Existing metric learning methods do not fully incorporate label semantics into modeling.
Approach:	They propose a method to largely improve metric learning for few-shot named entity recognition (NER) a pre-defined category is a key natural language understanding task .
Outcome:	The proposed method outperforms the previous state-of-the-art (SOTA) method with 16 of 18 settings outperformed previous methods by 9.12% and 34.51% .

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Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, Chunyan Miao

Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding (2022.emnlp-main)

Challenge:	Existing approaches to cross-lingual NER are labeled sequence translation and instance-based transfer via machine translation (MT) Existing methods to cross NER include label projection and labeling, but they are expensive and time-consuming.
Approach:	They propose a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids word order change and entity span determination.
Outcome:	The proposed method avoids word order change and entity span determination and can be generalized with the language-specific features from the target-language synthetic data and the language independent features from multilingual synthetic data.

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Rishabh Bhardwaj, Amrita Saha, Steven C.H. Hoi, Soujanya Poria

Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (2020.coling-main)

Challenge:	Prompt Tuning has been successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks.
Approach:	They propose to use a vector-quantized input-contextualized prompt as an extension to the soft prompt tuning framework to learn contextualization of soft prompt tokens.
Outcome:	The proposed prompt outperforms soft prompt tuning by an average margin of 1.19% on various language understanding tasks like SuperGLUE, QA, Relation classification, NER and NLI.

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Ngoc Phuoc An Vo, Irene Manotas, Vadim Sheinin, Octavian Popescu

When Morphology Hides in Plain Sight: Breaking the Isolation in Vietnamese and Beyond (2026.acl-long)

Challenge:	Identifying motion entities in text is not only challenging but beneficial for a better natural language understanding.
Approach:	They propose a Motion Entity Tagging model to identify entities in motion in a text using the Literal-Motion-in-Text dataset for training and evaluating the model.
Outcome:	The proposed method improves the Named-Entity Recognition task by splitting clauses and phrases from complex and long motion sentences.

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Anh Trac Duc Dinh, Khang Hoang Nhat Vo, Tai Tien Ta, Vinh Cong Doan, Tho Quan

KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi (2020.coling-main)

Challenge:	Adaptive Boundary-Token Fusion and a Morpheme-Aware Attention Bias are used to encode monosyllabic morphemes.
Approach:	They propose a morpheme-aware Transformer that augments a pretrained Vietnamese encoder with two lightweight inductive biases.
Outcome:	The proposed morpheme-aware Transformer outperforms strong baselines on Vietnamese POS, NER, and sentence-level classification benchmarks.

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Rubungo Andre Niyongabo, Qu Hong, Julia Kreutzer, Li Huang

HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction (2020.emnlp-main)

Challenge:	low-resource African languages are traditionally left behind because of the lack of well-annotated data and effective preprocessing.
Approach:	They propose two news datasets for multi-class classification of news articles in two low-resource African languages.
Outcome:	The proposed datasets show that training embeddings on the higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi.

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Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu

Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models (2025.acl-long)

Challenge:	Named Entity Recognition (NER) is a fundamental task in natural language processing due to the nature of the named entity.
Approach:	They propose a nested NER model that leverages two key properties pertaining to the named entity, including explicit boundary tokens and tight internal connection between tokens within the boundary.
Outcome:	The proposed model achieves state-of-the-art on three public NER datasets.

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Zhuojun Ding, Wei Wei, Chenghao Fan

DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)

Challenge:	Existing approaches to align large language models with information extraction tasks are costly and not all training data benefits target domains.
Approach:	They propose a framework which dynamically Selects and Merges expert models at inference time and combines experts beneficial to target domains.
Outcome:	The proposed framework outperforms the unified model by 10% on multiple benchmarks.

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Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao

Rethinking Negative Sampling for Handling Missing Entity Annotations (2022.acl-long)

Challenge:	Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages.
Approach:	They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base .
Outcome:	The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks.

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Yangming Li, Lemao Liu, Shuming Shi

Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning (2022.acl-long)

Challenge:	Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling.
Approach:	They propose an adaptive and weighted sampling distribution that further improves negative sampling by introducing missampling and uncertainty concepts.
Outcome:	The proposed approach improves on synthetic and well-annotated datasets in terms of F1 score and loss convergence.

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Kang Zhou, Yuepei Li, Qi Li

ELLEN: Extremely Lightly Supervised Learning for Efficient Named Entity Recognition (2024.lrec-main)

Challenge:	Existing methods for named entity recognition suffer from incomplete annotations due to incompleteness of external knowledge bases.
Approach:	They propose a method to solve the named entity recognition problem under distant supervision using dictionaries and knowledge bases.
Outcome:	The proposed method outperforms existing methods on two benchmark datasets labeled by various knowledge bases.

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Haris Riaz, Razvan Gabriel Dumitru, Mihai Surdeanu

CoMix: Guide Transformers to Code-Mix using POS structure and Phonetics (2023.findings-acl)

Challenge:	Named entity recognition (NER) tasks require an amount of annotations that are unrealistic for many real-world applications.
Approach:	They propose a semi-supervised named entity recognition method that blends language models with linguistic rules.
Outcome:	The proposed method outperforms most existing semi-supervised methods under the same supervision settings commonly used in the literature.

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Gaurav Arora, Srujana Merugu, Vivek Sembium

NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)

Challenge:	Existing multilingual transformer models lack the ability to intermix words of one language into the structure of another.
Approach:	They propose a pretraining approach to improve representation of code-mixed data in transformer models by incorporating phonetic signals, a modified attention mechanism and weak supervision guided generation by parts-of-speech constraints.
Outcome:	The proposed model improves performance across four code-mixed tasks and generalizes on out-of-domain translation.

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Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, James R. Curran

Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)

Challenge:	Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas.
Approach:	They describe a fine-grained, nested named entity dataset over the Wall Street Journal portion of the Penn Treebank.
Outcome:	The proposed dataset comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting.

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Xue Mengge, Bowen Yu, Zhenyu Zhang, Tingwen Liu, Yue Zhang, Bin Wang

Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories.
Approach:	They propose a NER-specific framework to inject coarse-to-fine named entity knowledge into pre-trained models by using a remote supervision strategy.
Outcome:	The proposed framework achieves significant improvements against several pre-trained base-lines, demonstrating its effectiveness in label-few and low-resource scenarios.

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Chen Jia, Yuefeng Shi, Qinrong Yang, Yue Zhang

Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)

Challenge:	Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER.
Approach:	They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT .
Outcome:	The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors.

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Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing

A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)

Challenge:	Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER.
Approach:	They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain.
Outcome:	The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively.

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Kun Liu, Shen Li, Daqi Zheng, Zhengdong Lu, Sheng Gao, Si Li

SmartSpanNER: Making SpanNER Robust in Low Resource Scenarios (2023.findings-emnlp)

Challenge:	Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks .
Approach:	They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance .
Outcome:	The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks.

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Min Zhang, Xiaosong Qiao, Yanqing Zhao, Shimin Tao, Hao Yang

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)

Challenge:	Named Entity Recognition (NER) is one of the most fundamental tasks in natural language processing.
Approach:	They propose a method which introduces a Named Entity Head (NEH) prediction task to SpanNER and performs multi-task learning together with task of span classification.
Outcome:	The proposed method improves the robustness of SpanNER in low resource scenarios on the CoNLL03, Few-NERD, GENIA and ACE05 benchmark datasets.

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Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi

Cross-Lingual Transfer

Challenge:	Recent pre-trained language models have achieved remarkable zero-shot performance . we propose a self-learning framework that utilizes unlabeled data of target languages .
Approach:	They propose a self-learning framework that utilizes unlabeled data of target languages to select silver labels for cross-lingual transfer tasks.
Outcome:	The proposed framework outperforms baseline models on two cross-lingual tasks by 10 F1 on average and 2.5 accuracy on natural language inference (NLI).

A Dataset of German Legal Documents for Named Entity Recognition (2020.lrec-1)

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Elena Leitner, Georg Rehm, Julian Moreno-Schneider

Where are we in Named Entity Recognition from Speech? (2020.lrec-1)

Challenge:	a dataset developed for Named Entity Recognition in German federal court decisions is available under a CC-BY 4.0 license.
Approach:	They describe a dataset developed for Named Entity Recognition in German federal court decisions.
Outcome:	The proposed dataset was developed for training an NER service for German legal documents in the EU project Lynx.

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Antoine Caubrière, Sophie Rosset, Yannick Estève, Antoine Laurent, Emmanuel Morin

Speech & Audio in NLP

Challenge:	Named entity recognition is usually made through a pipeline process that consists of processing audio and applying a NER to the audio outputs.
Approach:	They propose an original 3-pass approach and explore the capability of an E2E system to do structured NER.
Outcome:	The proposed system performs better than the current pipeline approach.

Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)

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Rodrigo Agerri, Yiling Chung, Itziar Aldabe, Nora Aranberri, Gorka Labaka, German Rigau

Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)

Challenge:	Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs.
Approach:	They propose to use statistical word alignments to project annotations from multiple sources to a target language.
Outcome:	The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language .

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Shuntaro Yada, Ayami Joh, Ribeka Tanaka, Fei Cheng, Eiji Aramaki, Sadao Kurohashi

Creating a Dataset for Named Entity Recognition in the Archaeology Domain (2020.lrec-1)

Challenge:	Current annotation policies for medical corpora are not standardized across clinical texts of different types.
Approach:	They propose to annotate medical records of various types using a named entity recognition (NER) task.
Outcome:	The proposed annotation scheme is applicable to large-scale clinical NLP projects.

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Alex Brandsen, Suzan Verberne, Milco Wansleeben, Karsten Lambers

Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation (2024.findings-acl)

Challenge:	Currently, there is no way to find 'by-catch', single finds of a different type, in the metadata of excavation reports.
Approach:	They propose to train NER classifiers on Dutch excavation reports to help archaeologists find structured information in archaic documents.
Outcome:	The proposed dataset contains 31k annotations between six entity types (artefact, time period, place, context, species & material).

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Joan Santoso, Patrick Sutanto, Billy Cahyadi, Esther Setiawan

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	Named Entity Recognition (NER) is an important task, but it requires a large amount of labeled data to perform well.
Approach:	They propose to use open-source Large Language Models to generate NER data with only a few labeled examples, reducing the cost of human annotations.
Outcome:	The proposed method significantly improves the baseline on diverse low-resource NER datasets and can be used to augment datasets with class-imbalance problems.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023.findings-acl)

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

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Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)

Challenge:	Existing augmentation techniques manipulate words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the text.
Approach:	They propose a novel Entity-to-Text based data augmentation technique called EnTDA to add, delete, replace or swap entities in the original text.
Outcome:	The proposed technique generates semantically coherent and entity preserving texts on thirteen NER tasks and two settings.

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Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan

Merge and Label: A Novel Neural Network Architecture for Nested NER (P19-1)

Challenge:	Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP).
Approach:	They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families.
Outcome:	The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families.

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Joseph Fisher, Andreas Vlachos

C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)

Challenge:	Named entity recognition (NER) is one of the best studied tasks in natural language processing.
Approach:	They propose a neural network architecture that merges tokens and/or entities into nested entities and labels them independently.
Outcome:	The proposed approach achieves state-of-the-art F1 of 74.6 and improves with contextual embeddings to 82.4.

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Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang, Shun Zhang, Jingang Wang, Zhoujun Li

A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)

Challenge:	Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples.
Approach:	They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations.
Outcome:	The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks.

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Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan

NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings (2025.findings-emnlp)

Challenge:	Named entity recognition (NER) is a fundamental task of information extraction.
Approach:	They propose to perform randomization tests on standard NER benchmarks to examine name regularity, mention coverage and context diversity.
Outcome:	The proposed model performs better on standard NER benchmarks than other models on open datasets.

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Or Shachar, Uri Katz, Yoav Goldberg, Oren Glickman

A Herd of Language Models Makes a Better Zero-shot Annotator for Clinical Named Entity Recognition (2026.findings-acl)

Challenge:	NER Retriever uses a user-defined type description to retrieve documents mentioning entities of that type.
Approach:	They propose a zero-shot retrieval framework for ad-hoc Named Entity Recognition . a user-defined type description is used to retrieve documents mentioning entities of that type .
Outcome:	The proposed framework outperforms lexical and dense retrieval baselines on three benchmarks.

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Seiji Shimizu, Shoko Wakamiya, Eiji Aramaki

Ontology-Style Relation Annotation: A Case Study (2020.lrec-1)

Challenge:	Clinical named entity recognition (NER) is a core task in clinical NLP.
Approach:	They propose a label-modeling method for Multi-LLM Annotation using R**epresentation learning to capture contextual similarity.
Outcome:	The proposed method improves the average F1 score by 8.6% over zero-shot baselines while reducing annotation costs.

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Savong Bou, Naoki Suzuki, Makoto Miwa, Yutaka Sasaki

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

Challenge:	Existing methods for Relation Extraction (RE) annotations use links between entities . a domain link connects the relation mention to the source entity while a range link connect the relation to the destination entity.
Approach:	They propose an Ontology-Style Relation (OSR) annotation approach to find relation mentions in relation annotations.
Outcome:	The proposed approach can be easily converted to Ontology RDF triples to populate an Ontologies.

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

Introducing Semantics into Speech Encoders (2023.acl-long)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang

Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset (2023.emnlp-main)

Challenge:	Existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Approach:	They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions.
Outcome:	The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%.

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Arthur Amalvy, Vincent Labatut, Richard Dufour

What do we really know about State of the Art NER? (2022.lrec-1)

Challenge:	Existing models for named entity recognition have limited range when applied to long documents . lack of supervision for such a task means one has to settle for unsupervised approaches.
Approach:	They propose to train a neural context retrieval model based on an instruction-tuned large language model.
Outcome:	The proposed method outperforms baselines on an English literary dataset . pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, but limited range when applied to long documents such as whole novels.

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Sowmya Vajjala, Ramya Balasubramaniam

ner and pos when nothing is capitalized (D19-1)

Challenge:	Named Entity Recognition (NER) is a well researched task and widely used in real world NLP scenarios.
Approach:	They perform a broad evaluation of Named Entity Recognition using a popular dataset that takes into consideration various text genres and sources constituting the dataset at hand.
Outcome:	The proposed models perform on a popular dataset and generate six new adversarial test sets through small perturbations in the original test set, replacing select entities while retaining the context.

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Stephen Mayhew, Tatiana Tsygankova, Dan Roth

NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data (2024.emnlp-main)

Challenge:	Named entity recognition and part of speech tagging require capitalization in training.
Approach:	They propose to modify only the casing of the train or test data using lowercasing and truecasing methods to modify the cassing of a model with high performance on both cased and uncased text.
Outcome:	The proposed model improves mention detection on noisy out-of-domain Twitter data by 8%.

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Sergei Bogdanov, Alexandre Constantin, Timothée Bernard, Benoit Crabbé, Etienne Bernard

Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025.coling-main)

Challenge:	Named Entity Recognition (NER) is a core component of natural language processing, present in a variety of applications such as medical coding, financial news analysis, or legal documents parsing.
Approach:	They propose to use Large Language Models (LLMs) to create NuNER, a compact language representation model specialized in the Named Entity Recognition task.
Outcome:	The proposed model outperforms similar-sized foundation models in the few-shot regime and is based on a human-annotated dataset.

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Ayoub Hammal, Benno Uthayasooriyar, Caio Corro

Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)

Challenge:	Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions.
Approach:	They propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data.
Outcome:	The proposed approach achieves state-of-the-art results in few-shot NER . it combines label supervision, cluster size constraints, and domain-specific discriminative subspace selection.

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Han Qin, Yuanhe Tian, Yan Song

BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (2025.coling-main)

Challenge:	Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous.
Approach:	They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs.
Outcome:	The proposed model improves on two English benchmark datasets and shows state-of-the-art performance.

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Quanjiang Guo, Yihong Dong, Ling Tian, Zhao Kang, Yu Zhang, Sijie Wang

SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems (L18-1)

Challenge:	Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect the entity spans of text and classify them into pre-defined set of entity types.
Approach:	They propose a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans.
Outcome:	The proposed framework outperforms prior methods and validates its effectiveness across a range of LLM architectures.

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Kevin Bowden, Jiaqi Wu, Shereen Oraby, Amita Misra, Marilyn Walker

From English to Code-Switching: Transfer Learning with Strong Morphological Clues (2020.acl-main)

Challenge:	UCSC researchers have developed an open domain social bot aimed at casual conversation . NER and NEL are important preprocessing steps for understanding user intent in open domain dialogue systems.
Approach:	They propose a tool for NER and NEL in open domain dialogue that addresses these challenges . they also propose two corpora based on 10,000 real user conversations .
Outcome:	The proposed open domain social bot is aimed at casual conversation.

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Gustavo Aguilar, Thamar Solorio

Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)

Challenge:	Linguistic code-switching (CS) is an understudied area in natural language processing . lack of resources and annotated data makes it difficult to strive for progress in CS-related tasks.
Approach:	They propose a method to adapt monolingual models to code-switched text in various tasks . they transfer English knowledge from a pre-trained ELMo model to different code-paired languages .
Outcome:	The proposed method outperforms multilingual BERT and homologous CS-unaware models and provides state-of-the-art in CS tasks.

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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Don’t Stop Fine-Tuning: On Training Regimes for Few-Shot Cross-Lingual Transfer with Multilingual Language Models (2022.emnlp-main)

Challenge:	Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities.
Approach:	They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy.
Outcome:	The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages.

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Fabian David Schmidt, Ivan Vulić, Goran Glavaš

SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition (2022.emnlp-main)

Challenge:	Recent work highlights the fallacies of zero-shot cross-lingual transfer with large multilingual models.
Approach:	They propose to replace sequential fine-tuning with joint fine-uning on source and target language instances.
Outcome:	The proposed techniques yield improved and more stable FS-XLT across the board.

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Fabian David Schmidt, Ivan Vulić, Goran Glavaš

Unsupervised Cross-lingual Representation Learning at Scale (2020.acl-main)

Challenge:	Large multilingual models fail to successfully transfer to low-resource languages for zero-shot cross-lingual transfer . sliced fine-tuning for named entity recognition (SLICER) forces stronger token contextualization in the Transformer.
Approach:	They propose a simple yet highly effective approach for improving zero-shot cross-lingual transfer for named entity recognition to low-resource languages.
Outcome:	The proposed approach reduces decontextualization of token representations and classifiers . it yields consistent transfer gains for low-resource languages, the authors show .

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Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov

Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference (2020.acl-main)

Challenge:	Pretraining multilingual language models at scale leads to performance gains for cross-lingual transfer tasks.
Approach:	They present a transformer-based multilingual masked language model pre-trained on 100 languages . they show that pretraining multilingual models at scale leads to significant performance gains .
Outcome:	The proposed model outperforms multilingual BERT (mBERT) on cross-lingual benchmarks.

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Jing Wang, Mayank Kulkarni, Daniel Preotiuc-Pietro

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (2020.acl-main)

Challenge:	Named entity recognition (NER) is a key component of many text processing pipelines.
Approach:	They propose a new architecture tailored to the task of identifying named entities with data from multiple genres.
Outcome:	The proposed architecture outperforms baseline and competitive methods on all three setups with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches.

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Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren

Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages (2024.emnlp-main)

Challenge:	Named entity recognition (NER) is a fundamental information extraction task that focuses on extracting entities from a given text and classifying them using pre-defined categories.
Approach:	They propose to use “entity triggers” to facilitate label-efficient learning of NER models.
Outcome:	The proposed model is significantly more cost-effective than the traditional neural NER frameworks.

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Jimin Sohn, Haeji Jung, Alex Cheng, Jooeon Kang, Yilin Du, David Mortensen

Learning In-context Learning for Named Entity Recognition (2023.acl-long)

Challenge:	Existing zero-shot cross-lingual NER approaches require substantial prior knowledge of the target language, which is impractical for low-resource languages.
Approach:	They propose a phonemic representation based on the International Phonetic Alphabet (IPA) to bridge the gap between representations of different languages.
Outcome:	The proposed method outperforms baseline models in low-resource languages with highest average F1 score and lowest standard deviation.

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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun

EconBERTa: Towards Robust Extraction of Named Entities in Economics (2023.findings-emnlp)

Challenge:	Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations.
Approach:	They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
Outcome:	The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors.

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Karim Lasri, Pedro Vitor Quinta de Castro, Mona Schirmer, Luis Eduardo San Martin, Linxi Wang, Tomáš Dulka, Haaya Naushan, John Pougué-Biyong, Arianna Legovini, Samuel Fraiberger

SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (2020.acl-main)

Challenge:	Adapting general-purpose language models to specific domains has proven to be effective in tackling downstream tasks such as impact evaluation.
Approach:	They propose to use EconBERTa, a large language model pretrained on scientific publications in economics, and ECON-IE, based on an expert-annotated dataset of economics abstracts for Named Entity Recognition (NER).
Outcome:	The proposed model outperforms EconBERTa on the downstream NER task and ECON-IE on the economics abstracts.

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Luoxin Chen, Weitong Ruan, Xinyue Liu, Jianhua Lu

Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)

Challenge:	Empirical studies show that virtual adversarial training (VAT) significantly improves the sequence labeling performance over baselines under supervised and semi-supervised settings.
Approach:	They propose a method which naturally applies VAT to sequence labeling models with conditional random field (CRF) Empirical studies show that SeqVAT significantly improves the sequence labelling performance over baselines under supervised settings, and outperforms state-of-the-art approaches under semi-supervised settings.
Outcome:	Empirical results show that the proposed method outperforms state-of-the-art approaches under semi-supervised settings.

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Hang Jiang, Yining Hua, Doug Beeferman, Deb Roy

Do “English” Named Entity Recognizers Work Well on Global Englishes? (2023.findings-emnlp)

Challenge:	Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature.
Approach:	They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk .
Outcome:	The proposed model outperforms existing models on Twitter and other social media platforms.

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Alexander Shan, John Bauer, Riley Carlson, Christopher Manning

DiZiNER: Disagreement-guided Instruction Refinement via Simulating Pilot Annotation for Zero-shot Named Entity Recognition (2026.acl-long)

Challenge:	Most of English named entity recognition datasets contain American or British English data . multiple problems may occur in low-resource English contexts, such as confusion of named entities with regionspecific meanings .
Approach:	They build a newswire dataset to analyze NER model performance on low-resource English variants . they find that models trained on the CoNLL or OntoNotes datasets experienced significant performance drops .
Outcome:	The results show that models trained on the CoNLL or OntoNotes datasets experienced significant performance drops.

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Siun Kim, Hyung-Jin Yoon

GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)

Challenge:	Large language models have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER) but their outputs still show persistent and systematic errors.
Approach:	They propose a framework that simulates the pilot annotation process and employs LLMs as both annotators and supervisors to refine model disagreements.
Outcome:	Using a pilot annotation process, the proposed framework outperforms its supervisor model on 18 benchmarks.

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Shuhe Wang, Yuxian Meng, Rongbin Ouyang, Jiwei Li, Tianwei Zhang, Lingjuan Lyu, Guoyin Wang

ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)

Challenge:	Existing sequence labeling algorithms can be decomposed into two parts .
Approach:	They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.
Outcome:	The proposed model performs well on three sequence labeling tasks.

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Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini

Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)

Challenge:	Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types .
Approach:	They propose a description-driven framework that enhances hard zero-shot NER in low-resource settings.
Outcome:	The proposed framework outperforms existing models by up to 16% in the F1 score . it also surpasses baseline models that use type names alone .

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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han

Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)

Challenge:	Existing methods to build named entity recognition systems with limited labeled data are lacking.
Approach:	They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited.
Outcome:	The proposed NER systems outperform existing methods on few-shot and training-free settings.

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Yandan Zheng, Anran Hao, Anh Tuan Luu

Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition (2025.findings-emnlp)

Challenge:	Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction.
Approach:	They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data.
Outcome:	The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks.

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Elena Merdjanovska, Alan Akbik

FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models (2023.emnlp-main)

Challenge:	Annotated datasets for supervised learning often contain incorrect labels, i.e. label noise.
Approach:	They compare popular sample metrics for detecting incorrect annotations in named entity recognition (NER) they find that training dynamics metrics work the best overall, and they detect errors that the model has not yet memorized .
Outcome:	The proposed measures reduce label noise across noise types by detecting errors in trained models.

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Konstantin Dobler, Gerard de Melo

Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)

Challenge:	Multilingual models have been released, but many of the world's languages are not covered.
Approach:	They propose a method that initializes the embedding matrix for a new tokenizer based on information in the source model's embeddable matrix.
Outcome:	The proposed method outperforms random initialization and previous work on language modeling and on a range of downstream tasks (NLI, QA, and NER).

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Yupeng Zhang, Shensi Wang, Peiguang Li, Guanting Dong, Sirui Wang, Yunsen Xian, Zhoujun Li, Hongzhi Zhang

SC-CoMIcs: A Superconductivity Corpus for Materials Informatics (2020.lrec-1)

Challenge:	Existing approaches to extract attribute values from product descriptions are incomplete and noisy due to the tedious nature of this task.
Approach:	They propose a framework to extract attributes from product descriptions to acquire implicit attributes in addition to the explicit ones.
Outcome:	The proposed framework outperforms existing methods on the extraction of implicit attribute values while achieving comparable performance for the explicit ones.

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Kyosuke Yamaguchi, Ryoji Asahi, Yutaka Sasaki

RaTEScore: A Metric for Radiology Report Generation (2024.emnlp-main)

Challenge:	Existing corpus of superconducting materials in Materials Informatics (MI) is limited.
Approach:	They propose to create a corpus tailored for the text mining of superconducting materials in Materials Informatics.
Outcome:	The proposed corpus can find terms relevant to a query term within a specified Named Entity category.

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Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, Weidi Xie

Text Generation

Challenge:	Existing metrics to evaluate the quality of medical reports are limited due to the complexity of clinical free-form texts.
Approach:	They propose a new metric to assess the quality of medical reports generated by AI models.
Outcome:	The proposed metric is based on a medical NER dataset and trained on NER models . it aligns more closely with human preference than existing metrics, the authors show .

Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction (2023.emnlp-main)

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Chong Zhang, Ya Guo, Yi Tu, Huan Chen, Jinyang Tang, Huijia Zhu, Qi Zhang, Tao Gui

Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition (2023.findings-emnlp)

Challenge:	Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs).
Approach:	They propose a method to predict token sequences within visually-rich documents by a simple prediction head.
Outcome:	The proposed method can be used to predict token mentions as token sequences within documents.

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Haocheng Luo, Wei Tan, Ngoc Nguyen, Lan Du

Know-Adapter: Towards Knowledge-Aware Parameter-Efficient Transfer Learning for Few-shot Named Entity Recognition (2024.lrec-main)

Challenge:	Existing active learning approaches focus on information-rich sequences, reducing the need for expert annotation.
Approach:	They propose a re-weighting-based active learning strategy that assigns dynamic weights to individual tokens.
Outcome:	The proposed strategy improves on multiple corpora and validates its effectiveness.

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Binling Nie, Yiming Shao, Yigang Wang

AniEE: A Dataset of Animal Experimental Literature for Event Extraction (2023.findings-emnlp)

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing.
Approach:	They propose a knowledgeable adapter to incorporate structure and semantic knowledge of knowledge graphs into PLMs for few-shot NER.
Outcome:	The proposed adapter improves the quality of retrieved information by adding explicit knowledge from external sources to PEFTs.

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Dohee Kim, Ra Yoo, Soyoung Yang, Hee Yang, Jaegul Choo

Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition (2024.lrec-main)

Challenge:	Event extraction (EE) is a crucial information extraction task in biomedical domain . existing biomedically EE datasets focus on cell experiments or overall procedures .
Approach:	They propose an animal experiment customized entity and event scheme for event extraction . they create an expert-annotated high-quality dataset containing discontinuous entities and nested events .
Outcome:	The proposed dataset is based on the animal experiment stage and a NER and EE model.

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Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin

People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts (2023.findings-acl)

Challenge:	Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP) yet, there is no open-source medical NER dataset specifically for Korean.
Approach:	They used ChatGPT to construct an open-source Korean NER dataset . they found 20% increase in medical NER performance compared to general Korean ner datasets.
Outcome:	The KBMC dataset shows an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets.

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Vit Novotny, Kristina Luger, Michal Štefánik, Tereza Vrabcova, Ales Horak

ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)

Challenge:	Pre-trained named entity recognition models are inaccurate on modern corpora due to differences in language OCR errors.
Approach:	They develop a named entity recognition (NER) corpus of 3.6M sentences from medieval charters written mainly in Czech, Latin, and German.
Outcome:	The proposed model achieves entity-level Precision of 72.81–93.98% with 58.14–81.77% Recall on a manually-annotated test dataset.

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Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu, Yinghao Li, Rongzhi Zhang, Chao Zhang

A Boundary Offset Prediction Network for Named Entity Recognition (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER).
Approach:	They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data.
Outcome:	The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities.

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Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, Yang Lin

NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing . span-based methods assign entity types to text spans, resulting in imbalanced sample space .
Approach:	They propose a method that predicts boundary offsets between candidate and nearest spans . the method integrates entity type and span representations to generate type-aware boundary offset .
Outcome:	The proposed method outperforms existing methods on eight widely-used NER datasets.

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Elena Merdjanovska, Ansar Aynetdinov, Alan Akbik

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)

Challenge:	Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries.
Approach:	They propose a noise-robust learning approach that learns from data with partially incorrect labels.
Outcome:	The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation.

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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu

Causal Intervention-based Few-Shot Named Entity Recognition (2023.findings-emnlp)

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples.
Approach:	They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE.
Outcome:	The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

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Zhen Yang, Yongbin Liu, Chunping Ouyang

Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (2024.emnlp-main)

Challenge:	Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples.
Approach:	They propose a causal intervention-based few-shot named entity recognition method that blocks the backdoor path between context and label.
Outcome:	The proposed method achieves state-of-the-art in a few-shot named entity recognition (NER) task.

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Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries (2024.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a key task in NLP to find mentions of named entities and classify them into predefined categories.
Approach:	They investigated the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks.
Outcome:	The data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting.

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Simona Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Cannon, Gerold Schneider, Benjamin Ineichen

Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval (2024.emnlp-main)

Challenge:	Despite substantial investment, developing new treatments for neurological conditions is a challenging and often unsuccessful endeavour.
Approach:	They propose a corpus for named entity recognition that is annotated clinical trial summaries from ClinicalTrials.gov.
Outcome:	The proposed corpus is annotated for neurological diseases, therapeutic interventions, and control treatments and achieves a close-to-human performance.

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Dae Yon Hwang, Bilal Taha, Harshit Pande, Yaroslav Nechaev

NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)

Challenge:	Existing methods for zero-shot learning are sparse, but have been used for dense retrieval (DR) .
Approach:	They propose a novel Universal Document Linking algorithm which links similar documents to enhance synthetic query generation across multiple datasets with different characteristics.
Outcome:	The proposed algorithm surpasses state-of-the-art methods in zero-shot cases.

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Fuhan Cai, Duo Liu, Zhongqiang Zhang, Ge Liu, Xiaozhe Yang, Xiangzhong Fang

ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)

Challenge:	Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy.
Approach:	They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies .
Outcome:	The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets.

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Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang

PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities (2024.lrec-main)

Challenge:	ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines .
Approach:	They propose a Macro-to-Micro progressive learning approach that improves UIE without external information.
Outcome:	ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone.

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Mariana O. Silva, Mirella M. Moro

GottBERT: a pure German Language Model (2024.emnlp-main)

Challenge:	Annotated corpus of 25 literary texts provides a rich set of annotations for Named Entity Recognition models.
Approach:	They propose an annotation dataset that simplifies the development of Named Entity Recognition models for Portuguese literary texts.
Outcome:	The proposed dataset simplifies the development of Named Entity Recognition models for Portuguese literary works.

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Raphael Scheible, Johann Frei, Fabian Thomczyk, Henry He, Patric Tippmann, Jochen Knaus, Victor Jaravine, Frank Kramer, Martin Boeker

Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data (2024.lrec-main)

Challenge:	Pre-trained language models have advanced natural language processing (NLP) despite the introduction of BERT, single-language models are still relevant.
Approach:	They present a German singlelanguage RoBERT model pre-trained exclusively on the German portion of the OSCAR dataset.
Outcome:	The GottBERT model outperforms the existing models on Named Entity Recognition and text classification tasks.

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Siyao Peng, Zihang Sun, Huangyan Shan, Marie Kolm, Verena Blaschke, Ekaterina Artemova, Barbara Plank

ToMMeR - Efficient Entity Mention Detection from Large Language Models (2026.acl-long)

Challenge:	Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects.
Approach:	They present the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles and tweets.
Outcome:	The proposed dataset improves on bar-wiki and moderately on bartweet with training first on Bavarian .

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Victor Morand, Nadi Tomeh, Josiane Mothe, Benjamin Piwowarski

Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics (2025.findings-emnlp)

Challenge:	Existing methods to detect text spans that refer to entities are often conflated with entity typing in a single joint task.
Approach:	They propose a lightweight model that probes mention detection capabilities from early LLM layers.
Outcome:	The proposed model achieves 93% recall zero-shot with 90% precision under human-calibrated LLM-judge protocol .

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Kavin R V, Pawan Goyal

SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)

Challenge:	Standard language models employ unique, monolithic embeddings for each token, limiting their ability to capture multifaceted meanings.
Approach:	They propose a compositional structure that accumulates diverse semantic facets for tokens . they apply this representational scheme to standard transformer architectures and a biomedical domain benchmark .
Outcome:	The proposed representational scheme achieves extreme compression in embedding parameters while maintaining >95% task performance relative to the base model.

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Yilun Zhu, Siyao Peng, Sameer Pradhan, Amir Zeldes

Data-Constrained Synthesis of Training Data for De-Identification (2025.acl-long)

Challenge:	Existing attempts to integrate singleton mention detection into end-to-end coreference resolution for English have been hampered by the lack of singletont mention spans in the OntoNotes benchmark.
Approach:	They propose a two-step neural mention and coreference resolution system that integrates singleton mentions with OntoNotes syntax trees to achieve a near approximation of the Ontonotes dataset with all singletont mentions.
Outcome:	The proposed system achieves 94% recall on a sample of gold singletons.

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Thomas Vakili, Aron Henriksson, Hercules Dalianis

TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition (2024.lrec-main)

Challenge:	sensitive domains lack widely available datasets due to privacy risks . recent studies have focused on evaluating the privacy of the synthetic text .
Approach:	They domain-adapt LLMs to clinical domain and generate synthetic clinical texts . they then generate NER models that can be annotated with tags for PII .
Outcome:	The proposed model performs better than the original model using smaller datasets.

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Yuanyuan Xu, Linhai Zhang, Deyu Zhou

Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)

Challenge:	Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner.
Approach:	They propose a span-based method with a controllable attention soft prompt for few-shot nested named entity recognition (TECA) the span part identification provides possible entity mentions without an extra filtering module.
Outcome:	The proposed method outperforms baseline models on four benchmark datasets and outperformed competing models on F1-score by 5.62% on ACE04, 5.11% on ace05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting.

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Qi Zhang, Fangping Lan, Cornelia Caragea, Longin Jan Latecki, Eduard Dragut

LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition (2025.emnlp-main)

Challenge:	Existing studies on ICL for Named Entity Recognition (NER) have mainly explored few-shot settings, but the potential of scaling to hundreds of demonstrations has not been thoroughly investigated.
Approach:	They evaluate various LLMs across multiple domains using hundreds of ICL examples and then assess the feasibility of using many-shot ICL as a data annotation framework.
Outcome:	The proposed framework can be scaled to hundreds of examples and annotate and refining data for low-resource NER tasks.

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Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi, Mark Dredze

UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)

Challenge:	Named Entity Recognition (NER) tasks are performed using only a few demonstrations.
Approach:	They propose a method that leverages training labels through token-level statistics to improve ICL performance.
Outcome:	The proposed method outperforms existing methods on five NER datasets and is robust in low-resource settings.

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Lauriane Aufrant, Lucie Chasseur

WkNER: Enhancing Named Entity Recognition with Word Segmentation Constraints and kNN Retrieval (2024.lrec-main)

Challenge:	Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default .
Approach:	They propose a new annotation scheme that offers higher comprehensiveness while preserving simplicity.
Outcome:	The proposed scheme offers higher comprehensiveness while preserving simplicity . it also includes an annotation tool to implement the scheme on the corpus UkraiNER .

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Yanchun Li, Senlin Deng, Dongsu Shen, Shujuan Tian, Saiqin Long

Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer (2025.emnlp-main)

Challenge:	Named Entity Recognition (NER) tasks require detecting the span and category of the entity from the text block.
Approach:	They propose a kNN retrieval enhancement algorithm that incorporates word segmentation information to enhance the model’s generalization ability and alleviate the problem of missing entity tokens in prediction.
Outcome:	The proposed method improves the performance of baseline models and achieves better or compared recognition accuracy than previous state-of-the-art models in multiple public Chinese and English datasets.

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Abteen Ebrahimi, Adam Wiemerslage, Katharina von der Wense

Just Use XML: Revisiting Joint Translation and Label Projection (2026.findings-acl)

Challenge:	NN-Rank is an algorithm for ranking source languages for cross-lingual transfer . it leverages hidden representations from multilingual models and unlabeled target-language data .
Approach:	They propose an algorithm for ranking source languages for cross-lingual transfer which leverages hidden representations from multilingual models and unlabeled target-language data.
Outcome:	The proposed algorithm outperforms state-of-the-art models on in-domain data and shows that it can achieve 92.8% of the NDCG achieved using all available target data.

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Thennal D K, Chris Biemann, Hans Ole Hatzel