Papers with Named Entity Recognition
PolyMinder: A Support System for Entity Annotation and Relation Extraction in Polymer Science Documents (2025.coling-demos)
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| 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. |
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
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| 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. |
Semantic Specialization of Distributional Word Vectors (D19-2)
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| Challenge: | Distributional word vectors conflate various paradigmatic and syntagmatic lexico-semantic relations. |
| Approach: | This tutorial provides an overview of specialization methods for distributional word vectors . a common solution is to include external lexico-semantic knowledge in a reshaped vector space . |
| Outcome: | This paper provides an overview of specialization methods for distributional word vectors . the most recent developments include a new method for asymmetric relations in Euclidean . |
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets (P18-3)
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| Challenge: | Named Entity Recognition (NER) is an important text analysis task . code-mixing occurs when lexical items and grammatical features from two languages appear in one sentence . |
| Approach: | They propose to use language identifiers, parts-of-speech tags and chunkers to analyze code-mixed data. |
| Outcome: | The proposed method outperforms the best baseline by 33.18%. |
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a task of detecting linguistically complex named entities in low-context text. |
| Approach: | They propose a keyword-based augmentation approach to address the context-entity mismatch issue in complex name recognition (NER) they use selective masking to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entity. |
| Outcome: | The proposed approach outperforms baseline methods on monolingual, cross-lingual, and multilingual complex NER in various low-resource settings. |
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages (2024.naacl-srw)
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| 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. |
GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) uses sequence labelling and span classification to identify entities. |
| Approach: | They propose a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction. |
| Outcome: | The proposed framework reduces the number of overlapping spans while maintaining competitive metric performance. |
Graph Propagation based Data Augmentation for Named Entity Recognition (2023.acl-short)
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| Challenge: | Synthetic data suffer from poor diversity, which leads to performance limitations. |
| Approach: | They propose a graph-propagated data augmentation framework for named entity recognition that uses graph propagation to build relationships between labeled data and unlabeled natural texts. |
| Outcome: | The proposed framework improves on a low-resource named entity recognition dataset. |
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Existing studies for named entity recognition focus on flat NER, i.e., without nested entities, by sequence labeling methods. |
| Approach: | They propose a Hierarchical Transformer network which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. |
| Outcome: | The proposed method achieves much better performance than the state-of-the-art approaches on GENIA, ACE-2004, ace-2005 and NNE datasets. |
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks (2022.emnlp-industry)
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| Challenge: | Domain Classification (DC) and Intent Classification/Named Entity Recognition (ICNER) are the most common methods for reducing teacher-student knowledge into manageable sizes for low-latency downstream applications. |
| Approach: | They investigate whether distillation from a generic LM benefits downstream tasks . a domain classification and a task-specific data set are used to fine tune the model . |
| Outcome: | The proposed model improves across tasks and test sets when only task-specific data is used. |
AVEN-GR: Attribute Value Extraction and Normalization using product GRaphs (2023.acl-industry)
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| Challenge: | Query Attribute Understanding (QAU) is a sub-component of QU that involves extracting named attributes from user queries. |
| Approach: | They propose a novel end-to-end approach that solves Named Entity Recognition and Entity Linking for QAU . they propose utilizing product graphs to enhance the representation of query entities . |
| Outcome: | The proposed approach solves Named Entity Recognition and Entity Linking and enables open-world reasoning for QAU. |
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task (D19-57)
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| Challenge: | Existing methods for natural language processing are labor-intensive and skill-dependent . Currently, most biomedical natural language tasks focus on English documents . |
| Approach: | They introduce a BERT benchmark to facilitate the research of PharmaCoNER task . they evaluate two baselines based on Multilingual BERT and BioBERT on the corpus . |
| Outcome: | The proposed task is based on multilingual BERT and BioBERT on the PharmaCoNER corpus. |
Modularized Interaction Network for Named Entity Recognition (2021.acl-long)
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| 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. |
Filtered Semi-Markov CRF (2023.findings-emnlp)
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| Challenge: | Existing methods for sequence labeling tasks such as Named Entity Recognition (NER) suffer from quadratic complexity over sequence length and poor performance compared to CRF. |
| Approach: | They propose a variant of Semi-Markov CRF that incorporates a filtering step to eliminate irrelevant segments, reducing complexity and search space. |
| Outcome: | The proposed method outperforms both CRF and Semi-CRF on several NER benchmarks while being significantly faster. |
ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation (2022.naacl-srw)
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| Challenge: | Existing models for the English language have been used to train on large corpus of high-quality texts. |
| Approach: | They present a pretrained Transformer-based encoder-decoder model for the Vietnamese language . they benchmark ViT5 on two downstream text generation tasks . |
| Outcome: | The proposed model outperforms existing models on Vietnamese Abstractive Summarization and Named Entity Recognition tasks. |
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network (2021.emnlp-main)
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| Challenge: | Existing methods to label data and identify entities require large amounts of manually annotated texts for training supervised models. |
| Approach: | They propose a dictionary extension method which extracts new entities through the type expanded model. |
| Outcome: | The proposed method outperforms state-of-the-art supervised systems on different types of datasets and surpasses supervised models. |
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)
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| 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. |
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. |
| Approach: | They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER . |
| Outcome: | The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages. |
SPY: Enhancing Privacy with Synthetic PII Detection Dataset (2025.naacl-srw)
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| Challenge: | Historically, Named Entity Recognition (NER) has been employed for PII detection, but PI I entities constitute a subset of NER entities. |
| Approach: | They propose to use Large Language Models to generate a synthetic dataset that emulates real-world PII scenarios and validate its quality. |
| Outcome: | The proposed dataset is validated and provides a benchmark for PII detection. |
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation (D19-3)
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| Challenge: | 80% of biomedical data is stored in unstructured text such as electronic health records (EHRs). |
| Approach: | They propose a web-based interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. |
| Outcome: | The proposed interface is designed to build, improve and customise a NER+L model for biomedical domain text and collate accurate research use case specific training data. |
Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts (2022.aacl-main)
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| 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 . |
Audio De-identification - a New Entity Recognition Task (N19-2)
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Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
| 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. |
T2NER: Transformers based Transfer Learning Framework for Named Entity Recognition (2021.eacl-demos)
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| Challenge: | Named entity recognition (NER) is an important task in information extraction due to large variations in entity names and flexibility in how entities are mentioned. |
| Approach: | They propose a Transformers based Transfer Learning framework for Named Entity Recognition (T2NER) that integrates transformer models with the state-of-the-art in NLP and provides a unified platform for transfer learning. |
| Outcome: | The proposed framework bridges the gap between the state-of-the-art in transformer models and the state of the art in NER with deep transformer models. |
Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)
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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li
| Challenge: | Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate . |
| Approach: | They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module. |
| Outcome: | The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks . |
When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)
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| Challenge: | Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment. |
| Approach: | They propose a pipeline for generating multilingual conversational NER datasets with minimal human validation and a framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data. |
| Outcome: | The proposed framework outperforms existing models on public and private conversations by 97.12% on CoNLL-2003 and 83.09% on OntoNotes 5.0. |
AdminSet and AdminBERT: a Dataset and a Pre-trained Language Model to Explore the Unstructured Maze of French Administrative Documents (2025.coling-main)
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| 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. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| 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 . |
SeqAttack: On Adversarial Attacks for Named Entity Recognition (2021.emnlp-demo)
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| Challenge: | Named Entity Recognition (NER) is a task of recognizing named entities in a chunk of text. |
| Approach: | They investigate the portability of adversarial attacks from text classification to named entity recognition and the ability of adversary training to counteract these attacks. |
| Outcome: | The proposed framework and web application can be used to cherry pick adversarial examples and perform character-level and word-level attacks. |
Effort Estimation in Named Entity Tagging Tasks (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) requires large amounts of annotated data. |
| Approach: | They investigate which variables influence the time spent on a named entity annotation task by a human . they found a root mean squared error (RMSE) of 25.68 words per minute with a Nearest Neighbors model . |
| Outcome: | The proposed model achieves a root mean squared error (RMSE) of 25.68 words per minute with a Nearest Neighbors model. |
A Study of the Importance of External Knowledge in the Named Entity Recognition Task (P18-2)
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| 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 . |
Zero-shot Cross-lingual Transfer With Learned Projections Using Unlabeled Target-Language Data (2023.acl-short)
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| Challenge: | Zero-shot cross-lingual transfer is enabled by pairing the language adapter in the target language with an appropriate task adapter within a source language. |
| Approach: | They propose to use unlabeled text to enhance zero-shot transfer by pairing language adapters with task adapters in a target language. |
| Outcome: | The proposed framework improves on three cross-lingual tasks with up to 11% relative improvement in Named Entity Recognition (NER), Question Answering (QA) and Natural Language Inference (NLI). |
Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
| Approach: | They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones. |
| Outcome: | The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| 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. |
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model (2024.emnlp-industry)
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| 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. |
Neural Modeling for Named Entities and Morphology (NEMO2) (2021.tacl-1)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. |
| Approach: | They develop a morphologically rich-and-ambiguous language with a token-level and morpheme-level NER annotation framework to address Named Entity Recognition (NER) a novel hybrid architecture precedes and prunes morphology and outperforms the standard pipeline for Hebrew NER and Hebrew morphologies. |
| Outcome: | The proposed architecture outperforms the standard pipeline for Hebrew NER and Hebrew morphological decomposition tasks. |
An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction (2022.aacl-short)
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| Challenge: | Entity and Relation Extraction tasks are often compared to pipeline approaches . a recent study shows that joint approaches can produce comparable results . |
| Approach: | They propose to use two approaches to the Entity and Relation Extraction task to compare their performance. |
| Outcome: | The proposed approach outperforms the best pipeline model but improperly designed approaches may have poor performance. |
Influence Functions for Sequence Tagging Models (2022.findings-emnlp)
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| 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. |
SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes (2025.acl-industry)
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| 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. |
How to tackle an emerging topic? Combining strong and weak labels for Covid news NER (2022.aacl-short)
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| Challenge: | Named Entity Recognition (NER) models can be trained for emerging topics such as medical domain where new topics are constantly evolving out of the scope of existing models and datasets. |
| Approach: | They propose a recipe to combine weak and strong labels to improve Named Entity Recognition (NER) models for emerging topics. |
| Outcome: | The proposed model outperforms methods trained on weak data while combining out-of-domain and in-domain weak label training. |
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
| Outcome: | The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set. |
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER (2021.acl-long)
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| Challenge: | Named entity recognition models rely on expensive labeled data for training, which is not always available across languages. |
| Approach: | They propose an adversarial approach where an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarially trained discriminators. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on standard benchmark datasets and outperformed existing methods on the target language. |
The Role of Global and Local Context in Named Entity Recognition (2023.acl-short)
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| Challenge: | Named Entity Recognition (NER) models are usually applied sequentially because of their complexity. |
| Approach: | They explore the impact of global document context on Named Entity Recognition . they find that correctly retrieving global document contextual has a greater impact . |
| Outcome: | The proposed model can retrieve global context better than leveraging local context . authors say the model can push the state of the art further . |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)
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| 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. |
AnnoHID: LLM-Assisted Annotation Framework for Low-Resource Medical Texts (2026.acl-demo)
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Annisa Maulida Ningtyas, Guntur Budi Herwanto, Yunita Sari, Rifki Afina Putri, Filip Kovacevic, Alaa El-Ebshihy, Varvara Arzt, Florina Piroi
| Challenge: | Social media platforms are a popular way to communicate with medical experts and improve health literacy. |
| Approach: | They introduce a semi-automated annotation framework for medical texts in low-resource languages . they use large language models for pre-annotation and human validation to support efficient annotation . |
| Outcome: | The proposed framework is applied to medical social media texts in Bahasa Indonesia . it yields higher inter-annotator agreement and human review improves output . future work focuses on mitigating pre-annotation bias and reducing annotation overhead . |
Decoding Text Spans for Efficient and Accurate Named-Entity Recognition (2026.acl-industry)
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| Challenge: | Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughput constraints in addition to strong accuracy. |
| Approach: | They propose a span-based NER framework that can be used to compute span representations at the final transformer stage, avoiding redundant computation in earlier layers. |
| Outcome: | The proposed framework matches competitive baselines while improving throughput and reducing computational cost. |
Reviving Iterative Refinement in Diffusion-based NER with an Initializer-Restorer Approach (2026.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Information Extraction. |
| Approach: | They propose a generative paradigm for Named Entity Recognition by modeling NER as a boundary diffusion process. |
| Outcome: | The proposed model performs better than baseline on ACE2004, GENIA, and CleanCoNLL. |
T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates (2023.tacl-1)
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| Challenge: | Named Entity Recognition (NER) has evolved from flat to overlapped and discontinuous . NER is a text recognition task that recognizes mentions that represent entities in text . |
| Approach: | They propose a two-stage span-based framework to solve a unified NER task using two stages . they extract entity spans, classify over all entity span pairs and combine them to train two stages. |
| Outcome: | The proposed framework beats all the current competitive baselines on eight benchmark datasets, obtaining the best performance of unified NER. |
Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs (2025.findings-naacl)
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| 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. |
Identifying Named Entities as they are Typed (2021.eacl-main)
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| 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. |
LIP-NER: Literal Patterns Benefit LLM-Based NER (2025.acl-srw)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use semantic information, but it is non-trivial to obtain literal patterns written in natural language. |
| Approach: | They propose an LLM-based NER framework that utilizes Literal Patterns to acquire literal patterns in natural language. |
| Outcome: | The proposed framework reduces human labor and provides a more efficient way to acquire literal patterns. |
Local Additivity Based Data Augmentation for Semi-supervised NER (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. |
| Approach: | They propose a Local Additivity based Data Augmentation method for semi-supervised Named Entity Recognition (NER) that creates virtual samples by interpolating sequences close to each other. |
| Outcome: | The proposed method improves both entity and context learning by adding to training data and extending it to semi-supervised setting. |
Debiasing Generative Named Entity Recognition by Calibrating Sequence Likelihood (2023.acl-short)
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| Challenge: | Existing approaches to recognize flat, overlapped and discontinuous entities uniformly have been used for Named Entity Recognition. |
| Approach: | They propose a reranking-based approach that redistributes the likelihood among candidate sequences depending on their performance via a contrastive loss. |
| Outcome: | The proposed method boosts baseline and yields competitive or better results compared with the state-of-the-art methods on 8 widely-used datasets for Named Entity Recognition. |
Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition (N19-1)
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| Challenge: | Current language models are unable to efficiently model entity names observed in text providing insufficient context. |
| Approach: | They propose to augment a traditional model with an external knowledge base to model entity names observed in text. |
| Outcome: | The proposed model improves on a Named Entity Recognition (NER) task by requiring no additional information such as named entity tags. |
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input (2021.naacl-main)
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| 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. |
Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables (2024.lrec-main)
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| Challenge: | Existing methods for detecting biases are biased because of confounding variables . authors propose a method to detect the biased classifier on any type of unlabeled data . |
| Approach: | They propose a method to detect biases of a specific fine-tuned classifier on unlabeled data. |
| Outcome: | The proposed method detects biases on unlabeled data on named entity perturbations . it uses name-entity recognition on target-domain data and morphosynctactically different languages spoken in relation to countries of the target groups . |
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA (2020.findings-emnlp)
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| Challenge: | Pretrained Language Models (PTLMs) are typically pretraining on target-domain text, which is expensive in terms of hardware, runtime and CO 2 emissions. |
| Approach: | They propose a faster, CPU-only domainadaptation method that trains Word2Vec on target-domain text and aligns the resulting word vectors with the wordpiece vectors of a general-domain PTLM. |
| Outcome: | The proposed method covers 60% of the BioBERT - BERT F1 delta, 5% of BioBERTS’s CO2 footprint and 2% of its cloud compute cost. |
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (2020.coling-main)
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| Challenge: | Existing methods for information extraction are based on pipelining to extract entities from unstructured judgment documents . a large number of judgment documents are released on China Judgments Online . |
| Approach: | They propose a legal triplet extraction system for drug-related criminal judgment documents . they annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain . |
| Outcome: | The proposed system extracts entities and semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. |
Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
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| 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. |
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data (2021.acl-long)
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| 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. |
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021.acl-long)
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| 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. |
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing. |
| Approach: | They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances. |
| Outcome: | Empirical results show that the proposed model improves against baselines and can be scaled to a large extent. |
Span-based Named Entity Recognition by Generating and Compressing Information (2023.eacl-main)
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| 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. |
Using Similarity Measures to Select Pretraining Data for NER (N19-1)
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| 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. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| 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. |
Attack Named Entity Recognition by Entity Boundary Interference (2024.lrec-main)
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| 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. |
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)
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| 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. |
Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a key task in Natural Language Processing, but most existing work on NER ignores the recognition of nested entities. |
| Approach: | They propose to annotate five web domains for nested named entities on top of the English Web Treebank (EWT) . they propose to use the English web treebank to perform cross-domain evaluations. |
| Outcome: | The proposed dataset covers five domains and includes transfer results from German and Danish. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| 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. |
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks (2024.eacl-long)
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| Challenge: | Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. |
| Approach: | They propose a prompt-based method for token-level sequence labeling tasks . they propose to decompose an input sentence into single tokens and apply one prompt template to each token. |
| Outcome: | The proposed method outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer . the method also attains state-of-the-art performance when employed with the mT5 model . |
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission (2024.findings-naacl)
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| Challenge: | Named entity recognition tasks are often suboptimal for NER . previous work focused on UE-NER, which estimates uncertainty scores for ner . |
| Approach: | They propose to use a Sequential Labeling Posterior Network to estimate uncertainty for NER . they propose to consider wrong-span cases and to evaluate the specificity of wrong-pan cases. |
| Outcome: | The proposed system improves on three datasets and AUPR on MIT-Restaurant datasets. |
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)
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| 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. |
Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)
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| 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. |
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models (C18-1)
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| Challenge: | Existing models for Named Entity Recognition (NER) are trained on data with the same NE label set, but they are not able to recognize previously unseen NE categories. |
| Approach: | They propose to use a sequence to sequence model for Named Entity Recognition (NER) and propose to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs. |
| Outcome: | The proposed model can recognize previously unseen NE categories while keeping the knowledge of previously seen categories. |
Calibrating Structured Output Predictors for Natural Language Processing (2020.acl-main)
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| Challenge: | Several modern machine-learning based NLP systems can provide a confidence score with their output predictions. |
| Approach: | They propose a general calibration scheme for output entities of interest in NLP applications that can be used to calibrate confidence scores. |
| Outcome: | The proposed calibration scheme outperforms current calibration techniques for Named Entity Recognition, Part-of-speech tagging and Question Answering systems. |
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) often suffers from insufficient labeled data when the number of annotations exceeds several tens of labels. |
| Approach: | They propose a model with a fine-to- coarse mapping matrix to leverage hierarchical structure explicitly. |
| Outcome: | The proposed model outperforms both K-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations. |
SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER (2022.coling-1)
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| Challenge: | Existing methods to solve Unlabeled Entity Problem (UEP) in Named Entities Recognition datasets are not effective in real-world datasets. |
| Approach: | They propose to decrease the distance of span representations with the same label while increasing it for different ones via span-based contrastive learning. |
| Outcome: | The proposed method outperforms the previous method on two real-world datasets. |
Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (2023.findings-acl)
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| 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. |
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)
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| Challenge: | Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks. |
| Approach: | They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods in zero-shot evaluation. |
A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)
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| 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. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| 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 . |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
ReCoT-NER: Enhancing Zero-Shot Named Entity Recognition through Chain-of-Thought Prompting and Recall-Oriented Loss Optimization (2026.findings-acl)
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| 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. |
Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for Named Entity Recognition (NER) are not able to learn Other-Class in the same way as new entity types. |
| Approach: | They propose a unified causal framework to retrieve causality from new entity types and Other-Class. |
| Outcome: | The proposed method outperforms the state-of-the-art method on three benchmark datasets. |
Extending Multilingual BERT to Low-Resource Languages (2020.findings-emnlp)
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| Challenge: | Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. |
| Approach: | They propose a simple but effective approach to extend multilingual BERT to any new language and show an increase in F1 on M-BERT and new languages. |
| Outcome: | The proposed approach improves on languages already in M-BERT and out of it on other languages. |
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025.coling-main)
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| Challenge: | Existing literature on nested entity recognition is insufficient partly due to insufficient annotated data. |
| Approach: | They propose a method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. |
| Outcome: | The proposed method significantly enhances entity recognition, matching state-of-the-art (SOTA) models without additional training data. |
Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) models rely on superficial entity patterns for predictions, without considering evidence from the context. |
| Approach: | They propose to de-bias NER datasets by altering entity-context distribution . they also validate the feasibility of the proposed de-bianking techniques . |
| Outcome: | The proposed methods can be applied to different models and improve existing models. |
Neural Entity Recognition with Gazetteer based Fusion (2021.findings-acl)
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| Challenge: | Named entity recognition systems can be applied to clinical domains where only limited data is accessible and interpretability is important. |
| Approach: | They propose to use auxiliary gazetteer model to fuse it with NER system . this allows for better robustness and interpretability across different clinical datasets . |
| Outcome: | The proposed model is data efficient and can adapt to new mentions in gazetteers without retraining. |
GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer (2024.naacl-long)
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| 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. |
A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction (2026.findings-eacl)
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Marco Martinelli, Stefano Marchesin, Vanessa Bonato, Giorgio Di Nunzio, Nicola Ferro, Ornella Irrera, Laura Menotti, Federica Vezzani, Gianmaria Silvello
| Challenge: | Existing biomedical IE benchmarks are narrow in scope and rely heavily on distantly supervised annotations. |
| Approach: | They propose a benchmark for Information Extraction (IE) that annotates entities, concept-level links, and relations manually from PubMed abstracts. |
| Outcome: | The GutBrainIE benchmark is based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. |
GLUECoS: An Evaluation Benchmark for Code-Switched NLP (2020.acl-main)
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| Challenge: | Recent studies show multilingual contextual embedding models perform better on cross-lingual and multilingual tasks. |
| Approach: | They propose to evaluate multilingual contextual embedding models on multilingual data . they use language identification from text, POS tagging, Named Entity Recognition and Question Answering . |
| Outcome: | The proposed benchmark evaluates models on language identification from text, POS tagging, Named Entity Recognition, Question Answering and a new task for code-switching, Natural Language Inference. |
Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)
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| Challenge: | Active Learning (AL) has been successfully applied to Deep Learning to drastically reduce the amount of data required to achieve high performance. |
| Approach: | They propose to query subsequences within sentences and propagate their labels to other sentences. |
| Outcome: | The proposed approach achieves high performance on OntoNotes 5.0 and CoNLL 2003 with only 13% of training data and 27% of the training data. |
MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)
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| 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. |
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). |
| Approach: | They compare three transformer-based names to two non-transformer-based ones . they find transformer-derived models incrementally outperform non-tranformer models . |
| Outcome: | The proposed models outperform the studied models in most domains with respect to the F1 score. |
Exploring Alignment in Shared Cross-lingual Spaces (2024.acl-long)
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| Challenge: | a new study examines the degree of alignment between languages in multilingual embeddings . cross-lingual embeds are designed to encode linguistic concepts that bridge equivalent semantic meaning . a comprehensive approach is needed to address these questions. |
| Approach: | They employ clustering to uncover latent concepts within multilingual models . they introduce two metrics to quantify alignment and overlap of these concepts . |
| Outcome: | The proposed model can capture linguistic nuances across languages, but is not language-agnostic? the proposed model is able to capture nuances in multiple languages, the authors say. |
ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER (2023.findings-acl)
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| 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. |
DiFiNet: Boundary-Aware Semantic Differentiation and Filtration Network for Nested Named Entity Recognition (2024.acl-long)
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| Challenge: | Existing approaches to Named Entity Recognition focus on identifying non-nested entities, but there is no explicit guidance for boundary detection. |
| Approach: | They propose a Boundary-aware Semantic Differentiation and Filtration Network for nested NER that leverages a biaffine attention mechanism to generate a span representation matrix. |
| Outcome: | Extensive experiments on three benchmark datasets demonstrate the proposed model yields a new state-of-the-art performance. |
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition (D19-1)
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| Challenge: | Existing work on name-switching focuses on word-level aspects but neglects subword-level characteristics shared across languages. |
| Approach: | They propose hierarchical meta-Embeddings that combine word-level and subword-level embeddings to create language-agnostic lexical representations. |
| Outcome: | The proposed model achieves state-of-the-art in English-Spanish code-switching scenarios. |
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations (D18-1)
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| 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 . |
Developing New Linguistic Resources and Tools for the Galician Language (L18-1)
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| Challenge: | Existing resources and tools for the Galician language are lacking for other less-resourced languages, such as statistical tools for lemmatization and Named Entity Recognition. |
| Approach: | They propose to develop a manually revised corpus for POS tagging and lemmatization, and a new manually annotated corpus to train existing statistical tools for the Galician language. |
| Outcome: | The proposed resources include a new corpus for POS tagging and lemmatization, and a manually annotated corpus to handle Named Entity recognition. |
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets (2024.lrec-main)
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| Challenge: | Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities for downstream tasks such as Named Entity Recognition (NER) challenges persist in MLLM implementations that are not cross-linguistically robust. |
| Approach: | They evaluate two well-known MLLMs on 13 pairs of languages with a geographic, genetic, or borrowing relationship. |
| Outcome: | The proposed models show that they can leverage information acquired in a source language and apply it to a target language. |
Aligning Multilingual Embeddings for Improved Code-switched Natural Language Understanding (2022.coling-1)
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| Challenge: | a recent study has shown that multilingual models can be effective on monolingual data but need additional training to work well with code-switched text. |
| Approach: | They propose to train multilingual models with alignment objectives using parallel text . they find such an explicit alignment step improves performance on code-switched NLP tasks . |
| Outcome: | The proposed model improves on Hindi-English Sentiment Analysis, Named Entity Recognition and Question Answering tasks. |
Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models (2024.lrec-main)
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| 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. |
NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is used in a variety of downstream tasks in the biomedical domain, but is difficult when working with consumer health questions (CHQs). |
| Approach: | They propose to use a dataset to identify named entities in health-related texts in Bengali to address the scarcity of available data. |
| Outcome: | The proposed dataset captures the diverse range of linguistic styles and dialects used by native speakers from various regions in their day-to-day lives. |
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)
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| 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 . |
Applying Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages (2022.coling-1)
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| Challenge: | Existing approaches to build NLP models for low-resourced languages rely on machine translation or cross-lingual transfer. |
| Approach: | They propose to use natural annotations to build synthetic training sets from resources not originally designed for the target downstream task. |
| Outcome: | The proposed model achieves the F1 score of 0.78 for Belarusian starting from zero resources compared to the baseline of 0.63 for English . the proposed model can be fine-tuned to reflect linguistic properties, such as the grammatical case and gender, for the Slavic languages. |
Constrained Labeled Data Generation for Low-Resource Named Entity Recognition (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) in lowresource languages has been a challenge for years . Existing methods suffer from low quality of annotated data in target language . |
| Approach: | They propose a method that uses projected annotations to generate pseudo supervised data with a transformer language model and a constrained beam search. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance in low-resource languages. |
Enhanced Entity Annotations for Multilingual Corpora (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a new language for natural language processing. |
| Approach: | They propose to improve the annotation quality of the English Wikipedia tool WEXEA . they propose to use a proven NER system to annotate entities in Wikipedia . |
| Outcome: | The proposed tool can be used to exhaustively annotate entities in Wikipedia articles. |
Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting (2024.findings-emnlp)
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| 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. |
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning (2023.emnlp-main)
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| Challenge: | Large language models have demonstrated their capability with few-shot inference . however, in-domain demonstrations are not always available in real scenarios . |
| Approach: | They propose unsupervised domain adaptation problem to adapt language models from source domain to target domain without any target labels. |
| Outcome: | The proposed model performs better than baseline models on Sentiment Analysis and Named Entity Recognition tasks. |
COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing (2025.findings-emnlp)
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| 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. |
Taxonomy Expansion for Named Entity Recognition (2023.emnlp-main)
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Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, Miguel Ballesteros
| Challenge: | Training a Named Entity Recognition model involves fixing a taxonomy of entity types . however, requirements evolve and a model may need to recognize additional entity types. |
| Approach: | They propose a method that uses only partially annotated datasets to train a model to recognize additional entity types. |
| Outcome: | The proposed approach performs better with partially annotated datasets than other approaches . the gap between the proposed approach and other approaches is large in additional datasets . |
Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition (2021.emnlp-main)
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| Challenge: | Existing methods augment input sequence with token replacement, assuming annotations on the replaced positions are unchanged. |
| Approach: | They propose to use paraphrasing to enhance unsupervised consistency training by replacing tokens with augmented data. |
| Outcome: | The proposed method is especially effective when annotations are limited. |
Hierarchical Region Learning for Nested Named Entity Recognition (2020.findings-emnlp)
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| Challenge: | Existing methods to recognize entities recursively from innermost to outermost are based on brute force and two-stage paradigms, often leading to cascaded errors. |
| Approach: | They propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. |
| Outcome: | Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA show that the proposed framework performs better than state-of-the-art models. |
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning (2023.emnlp-main)
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| Challenge: | Named Entity Recognition, Relation Extraction, Semantic Role Labeling are examples of sequence labeling problems that require finetuning to the target format. |
| Approach: | They propose a dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples. |
| Outcome: | The proposed approach improves performance in low-resource settings and in extreme low-level settings. |
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan (2021.findings-acl)
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Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas
| Challenge: | Multilingual language models have been a crucial breakthrough for under-resourced languages . however, the superiority of language-specific models has already been proven for underresourced ones . |
| Approach: | They propose to build a monolingual monolingual model that is comparable to state-of-the-art large multilingual models. |
| Outcome: | The proposed model consistently outperforms state-of-the-art models across tasks and settings. |
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning (2022.acl-long)
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| Challenge: | Existing methods for Named Entity Recognition only learn class-specific semantic features and intermediate representations from source domains, resulting in suboptimal performance. |
| Approach: | They propose a contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. |
| Outcome: | The proposed technique outperforms existing methods by 3%-13% absolute F1 points while showing consistent performance trends. |
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020.findings-emnlp)
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Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul N.C., Avik Bhattacharyya, Mitesh M. Khapra, Pratyush Kumar
| Challenge: | In this paper, we present NLP resources for 11 major Indian languages . distributional representations are the cornerstone of modern NLP, authors say . |
| Approach: | They introduce NLP resources for 11 major Indian languages from two major language families . monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . they also compile a benchmark for Indian language NLU to evaluate their results . |
| Outcome: | The monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . the pre-trained language models are based on the compact ALBERT model . |
A Unified Generative Framework for Various NER Subtasks (2021.acl-long)
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| 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. |
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER (2021.acl-long)
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| 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. |
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter (2021.acl-long)
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| Challenge: | Existing methods for Chinese sequence labelling only fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. |
| Approach: | They propose a Lexicon Enhanced BERT model which integrates external lexicon knowledge into BERT layers directly by a lexiccon Adapter layer. |
| Outcome: | The proposed model integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. |
CNER: Concept and Named Entity Recognition (2024.naacl-long)
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Approach: | They propose a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Outcome: | The proposed task gains +5.4 and +8 macro F1 points when performed as a unified task compared to specialized named entity and concept recognition systems. |
Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets (2023.findings-acl)
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| Challenge: | a federated domain adaptation approach is used to learn with NER datasets from multiple platforms while not violating data privacy. |
| Approach: | They propose to use a distillation approach to facilitate knowledge transfer across platforms. |
| Outcome: | The proposed model performs better in the clinic domain. |
HiNER: A large Hindi Named Entity Recognition Dataset (2022.lrec-1)
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Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
| Approach: | They propose to use a standard-abiding Hindi NER dataset to analyze the annotations of a class of naming entities in free text. |
| Outcome: | The proposed dataset achieves a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set. |
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction (2020.emnlp-main)
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| 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. |
Multilingual Sequence-to-Sequence Models for Hebrew NLP (2023.findings-acl)
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| Challenge: | Recent work on pretrained language models for Hebrew is under-parameterized and under-trained . previous work on pretraining Hebrew LMs focused on encoder-only architectures . |
| Approach: | They propose to use sequence-to-sequence generative architectures to train large LMs in morphologically rich languages such as Hebrew. |
| Outcome: | The proposed model improves on all existing Hebrew NLP benchmarks. |
Exploring Nested Named Entity Recognition with Large Language Models: Methods, Challenges, and Insights (2024.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
| Approach: | They investigate the application of Large Language Models (LLMs) to nested NER . they find methodologies from previous work are less effective . |
| Outcome: | The proposed methods outperform BERT-based models in nested NER tasks . however, they do not outperformed the existing models on the GENIA dataset . |
Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)
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| 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. |
An Effective Transition-based Model for Discontinuous NER (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) data sets often contain mentions consisting of discontinuous spans. |
| Approach: | They propose a transition-based model with generic neural encoding for discontinuous NER that can recognize discontinuous mentions without sacrificing the accuracy on continuous mentions. |
| Outcome: | The proposed model can recognize discontinuous mentions without sacrificing accuracy on continuous mentions. |
ESPVR: Entity Spans Position Visual Regions for Multimodal Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Existing methods for acquiring local visual information are limited . existing methods for named entity recognition are redundant or insufficient . |
| Approach: | They propose an Entity Spans Position Visual Regions module to obtain visual regions corresponding to entities in the text. |
| Outcome: | The proposed method achieves the SOTA on Twitter-2017 and competitive results on Twitter 2015 . previous efforts have yielded promising results, but they still fall short in selecting visual information. |
Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition (2022.lrec-1)
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| Challenge: | Word embeddings can capture the semantics of words and other hidden features, but the Arabic language is complex and requires a large amount of information to process. |
| Approach: | They propose to add morphological and syntactical features to Arabic word embeddings to train the model. |
| Outcome: | The proposed model outperforms the previous systems to the best of our knowledge. |
SmartSpanNER: Making SpanNER Robust in Low Resource Scenarios (2023.findings-emnlp)
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| 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. |
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)
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| 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). |
Enriching a Time-Domain Astrophysics Corpus with Named Entity, Coreference and Astrophysical Relationship Annotations (2024.lrec-main)
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| Challenge: | Existing corpora for astrophysical natural language processing are limited to Named Entity Recognition tasks, leaving a gap in resource diversity. |
| Approach: | They propose to expand astroECR to cover named entities, coreferences, annotations related to aastrphysical relationships, and normalizing celestial object names. |
| Outcome: | The proposed model extends the time-domain astrophysics corpus to include named entities, coreferences, and annotations related to aastrphysical relationships. |
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) models are crucial for academic writing . existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types . |
| Approach: | They propose to annotate 100 full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets. |
| Outcome: | The proposed model can be used to identify 10 entity types in scientific articles . existing models cannot recognize fine-grained models like ML models and model architecture . |
A Dataset of German Legal Documents for Named Entity Recognition (2020.lrec-1)
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| 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. |
Named Entities in Medical Case Reports: Corpus and Experiments (2020.lrec-1)
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| Challenge: | Only very few annotated corpora in the medical domain exist. |
| Approach: | They propose to annotate medical entities in case reports from PubMed Central's open access library. |
| Outcome: | The proposed corpus is the first of its kind to be made available to the scientific community in English. |
Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| 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 . |
SpanNER: Named Entity Re-/Recognition as Span Prediction (2021.acl-long)
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| Challenge: | Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. |
| Approach: | They experimentally implement 154 named entity recognition models on 11 datasets and show that span prediction can serve as a system combiner to re-recognize named entities from different systems’ outputs. |
| Outcome: | The proposed model can be used to re-recognize named entities from different systems’ outputs. |
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a task within the field of Natural Language Processing that deals with the identification and categorization of Named entities (NEs) in a given text. |
| Approach: | They propose to use vector and tensor embeddings to train Portuguese Named Entity Recognition (NER) in the Geology domain. |
| Outcome: | The proposed model achieves state-of-the-art for the Portuguese Geology domain with one of its embeddings. |
Evaluating Performance of Pre-trained Word Embeddings on Assamese, a Low-resource Language (2024.lrec-main)
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| Challenge: | Word embeddings are not explored in high-resource languages such as Assamese, where resources are limited. |
| Approach: | They propose to use assamese pre-trained word embeddings for sequence labeling tasks such as Parts-of-speech and Named Entity Recognition to evaluate their performance. |
| Outcome: | The proposed embeddings outperform the existing methods on Parts-of-speech and Named Entity Recognition tasks. |
Reconstructing NER Corpora: a Case Study on Bulgarian (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
| Outcome: | The proposed model improves a type-based Named Entity Recognition (NER) training corpus and predicts and annotates new types in a test corpus. |
Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation (2024.findings-acl)
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| 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. |
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways (P19-1)
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| Challenge: | a general method for the interpretation and comparison of neural models is proposed . we factor a complex neural model into its functional components . |
| Approach: | They propose a method that factored a complex neural model into its functional components . they use correlated task level and linguistic heuristics to identify correlated pathways . |
| Outcome: | The proposed method can be applied in a purely post-processing manner to understand neural models. |
CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) tasks require large labeled datasets to perform well. |
| Approach: | They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them. |
| Outcome: | The proposed model outperforms strong weak-supervision-based models by 6.5 F1 points . the proposed model can learn from limited labeled data and perform better on small datasets . |
Named Entity Recognition as Dependency Parsing (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. |
| Approach: | They propose a method to handle both types of NEs in one system by using a biaffine dependency parsing model which scores pairs of start and end tokens in a sentence. |
| Outcome: | The proposed model performs well on 8 corpora and achieves accuracy gains of up to 2.2 percentage points. |
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
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Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| 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. |
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration (2023.findings-acl)
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| Challenge: | Previous cross-lingual transfer methods are limited to orthographic representation learning via textual scripts. |
| Approach: | They propose a phonemic transcription framework that incorporates phonemic translations as an additional linguistic modality beyond the orthographic transcriptions for cross-lingual transfer. |
| Outcome: | The proposed framework captures local one-to-one alignment between two different modalities and integrates bilingual dictionaries into multilingual contexts. |
Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (2024.emnlp-main)
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| Challenge: | Existing methods for extracting structured data from unstructured texts neglect unique features of the biomedical literature, such as ambiguous entities and nested proper nouns. |
| Approach: | They propose a model that leverages sentence-level relation classification before entity extraction to tackle entity ambiguity. |
| Outcome: | The proposed model outperforms baselines in both NER and RE tasks and has competitive performance compared to the state-of-the-art fine-tuned baselines for RE. |
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies (2020.lrec-1)
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| Challenge: | Language Models (LMs) are an oft studied area of natural language processing . Word Embeddings (WE) are vector space representations of a vocabulary . |
| Approach: | They evaluate Word Embeddings (WE) models for the Portuguese langauage . results show that a diverse corpus can often outperform a larger, less textually diverse corp. |
| Outcome: | The proposed models outperform a larger, less textually diverse corpus in two tasks . the evaluation shows that a diverse and comprehensive corpus outperformed a smaller, less diverse corp. |
Ontology-Style Relation Annotation: A Case Study (2020.lrec-1)
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| 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. |
Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language (2024.lrec-main)
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| Challenge: | Large Language Models excel in various tasks like Named Entity Recognition and Part-of-Speech tagging. |
| Approach: | They propose to use large language models to perform NLP tasks such as Named Entity Recognition and Part-of-Speech tagging in Nepali. |
| Outcome: | The proposed models perform better than other approaches for Nepali NER and POS tagging tasks. |
Extracting Biomedical Entities from Noisy Audio Transcripts (2024.lrec-main)
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Nima Ebadi, Kellen Morgan, Adrian Tan, Billy Linares, Sheri Osborn, Emma Majors, Jeremy Davis, Anthony Rios
| Challenge: | Named Entity Recognition (NER) is particularly affected by noise, often termed the ASR-NLP gap. |
| Approach: | They propose a dataset to bridge the ASR-NLP gap in the biomedical domain by extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. |
| Outcome: | The proposed method can clean 2,000 clean and noisy recordings and eliminate errors using zero-shot and few-shot methods. |
What do we really know about State of the Art NER? (2022.lrec-1)
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| 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. |
ner and pos when nothing is capitalized (D19-1)
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| 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%. |
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data (2024.emnlp-main)
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| 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. |
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020.emnlp-main)
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| Challenge: | Existing active sequence labeling methods use the queried samples alone in each iteration, which is inefficient for leveraging human annotations. |
| Approach: | They propose a data augmentation method to augment queried samples by generating extra labeled sequences in each iteration. |
| Outcome: | The proposed method improves the standard active sequence labeling method by 2.27%–3.75% in terms of F1 scores. |
GRIT: A Dataset of Group Reference Recognition in Italian (2024.lrec-main)
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| Challenge: | a task of automatically recognizing group references has not yet gained much attention within NLP. |
| Approach: | They propose a large-scale dataset for automatic group reference recognition in italian . they verify the validity of the task using a fine-tuned BERT model . |
| Outcome: | The proposed dataset proves that it can be applied to political text analysis and social media analysis. |
Portuguese Named Entity Recognition using Conditional Random Fields and Local Grammars (L18-1)
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| Challenge: | Named Entity Recognition (NER) involves automatically identifying and classifying entities such as persons, places, organizations and values. |
| Approach: | They propose to use Conditional Random Fields for Named Entity Recognition in Portuguese texts using a Local Grammar as an additional informed feature. |
| Outcome: | The proposed method outperforms competing systems in the literature. |
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems (L18-1)
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| 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. |
AxomiyaBERTa: A Phonologically-aware Transformer Model for Assamese (2023.findings-acl)
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| Challenge: | AxomiyaBERTa is a novel BERT model for low-resource languages . Transformers require extensive computing resources and suffer in low-compute settings . |
| Approach: | They propose a novel BERT model for Assamese, a morphologically-rich low-resource language of eastern India that is trained on a simple masked language modeling task without the NSP objective. |
| Outcome: | The proposed model performs well on token-level tasks and on “longer context” tasks with the aid of embedding disperser and phonological signals. |
EconBERTa: Towards Robust Extraction of Named Entities in Economics (2023.findings-emnlp)
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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
| 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. |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
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| 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. |
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| 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. |
Pre-training and Evaluating Transformer-based Language Models for Icelandic (2022.lrec-1)
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| Challenge: | Pre-trained models obtain state-of-the-art performance on a wide variety of NLP tasks, including Question Answering (QA), Named Entity Recognition (NER), Part-of Speech (POS) tagging and Automatic Text Summarization (ATS). |
| Approach: | They pre-train four types of monolingual ELECTRA and ConvBERT models and compare them to a previously trained monolingual RoBERTa model and multilingual mBERT model. |
| Outcome: | The models outperform a multilingual model on four downstream tasks. |
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)
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| 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. |
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation (2024.lrec-main)
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| Challenge: | Existing approaches to modularity are limited to the case of pre-trained modules in a pre-training language model. |
| Approach: | They propose a method that allows the transfer of pre-trained PEFT modules between incompatible PLMs without any change in the inference complexity. |
| Outcome: | The proposed method allows the transfer of modules between incompatible PLMs without any change in the inference complexity. |
Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) tasks are becoming more challenging due to the introduction of complex tagsets, which often leads to the failure of existing NER systems in accurately recognizing these entities. |
| Approach: | They propose a novel attack which relies on disentanglement and word attribution techniques to learn an embedding and identifying important words across both components. |
| Outcome: | The proposed approach improves the F1 score over the original LLM model by 8% and 18% on CoNLL-2003 and Ontonotes 5.0 datasets respectively. |
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
| Challenge: | Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs. |
| Approach: | They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods. |
| Outcome: | The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. |
The Less the Merrier? Investigating Language Representation in Multilingual Models (2023.findings-emnlp)
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| Challenge: | Multilingual models can be used to integrate multiple languages into one model and use cross-language transfer learning to improve performance for different NLP tasks. |
| Approach: | They propose to include languages in popular multilingual models and to use cross-language transfer learning to improve performance for different NLP tasks. |
| Outcome: | The proposed models perform better on downstream tasks for seen and unseen languages than community-centered models for low-resource languages. |
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition (2023.findings-emnlp)
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| 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. |
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition (2024.lrec-main)
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Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin
| 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. |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
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Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Standard English and Malaysian English exhibit significant differences in morphosyntactic variations . existing datasets are not sufficient to enhance NLP tasks in Malaysian english . |
| Approach: | They propose to use a Malaysian English news article dataset to refine NER models for Malaysian english. |
| Outcome: | The proposed dataset can improve the performance of NER on Malaysian English. |
ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language (2024.lrec-main)
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| Challenge: | a new study examines the impact of natural language processing (NLP) on the endangered Manchu language. |
| Approach: | They propose to use BiLSTM-CRF, BERT, and mBERT to train transformer-based models on Manchu for NER and POS tagging tasks. |
| Outcome: | The proposed models achieved over 90% F1 score in both NER and POS tasks. |
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)
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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu
| 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. |
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (2024.emnlp-main)
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| 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. |
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities (2024.lrec-main)
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| 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. |
GottBERT: a pure German Language Model (2024.emnlp-main)
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Raphael Scheible, Johann Frei, Fabian Thomczyk, Henry He, Patric Tippmann, Jochen Knaus, Victor Jaravine, Frank Kramer, Martin Boeker
| 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. |
EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval (2026.findings-acl)
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| Challenge: | Existing lightweight approaches to retrieval-augmented generation fail to capture latent semantic connections between disjoint entities. |
| Approach: | They propose a lightweight RAG framework that constructs a hypergraph capturing both structure and semantic relationships using a hybrid structural-semantic retrieval mechanism. |
| Outcome: | EHRAG outperforms state-of-the-art methods on four datasets while maintaining zero token consumption. |
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data (2024.lrec-main)
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Siyao Peng, Zihang Sun, Huangyan Shan, Marie Kolm, Verena Blaschke, Ekaterina Artemova, Barbara Plank
| 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 . |
text2story: A Python Toolkit to Extract and Visualize Story Components of Narrative Text (2024.lrec-main)
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| Challenge: | Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law. |
| Approach: | They propose to use an array of narrative extraction tools to extract narratives from text . the package contains an array and an experimental module for evaluation . |
| Outcome: | The text2story python supports the narrative extraction and visualization pipeline. |
Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)
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| 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. |
LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition (2025.emnlp-main)
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| 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. |
Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
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| Challenge: | Clinical coding is labor-intensive and prone to delays, leading to global backlogs. |
| Approach: | They propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
| Outcome: | The proposed approach reduces training time by over half on a standard evaluation dataset compared to current methods . it uses Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: The Case of NER in French (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is an applicative task for which annotation schemes vary . a lack of robustness of some tools towards textual variation limits evaluation . |
| Approach: | They propose a gold corpus for french annotated with a rich tagset that enables comparison with multiple annotation schemes. |
| Outcome: | The proposed framework enables a fair comparison of NER systems across textual genres and annotation schemes. |
AnchorAlign: A Novel Anchor Alignment-enhanced Generative Method for Joint Named Entity Recognition and Relation Extraction (2026.findings-acl)
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| Challenge: | Named Entity Recognition and Relation Extraction are interdependent tasks in information extraction. |
| Approach: | They propose a generative method enhanced by anchor alignment to bridge NER and RE tasks . they use anchor entities as semantic pivots to align the two tasks based on their semantic representations . |
| Outcome: | The proposed method outperforms state-of-the-art models on five benchmark datasets. |
Interpretable Coreference Resolution Evaluation Using Explicit Semantics (2026.acl-long)
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| Challenge: | Existing evaluation methods for coreference resolution are limited by semantic and contextual information. |
| Approach: | They propose a semantically-enhanced evaluation framework for coreference resolution that assigns semantic labels to nominal mentions and propagates them to entire coreference clusters. |
| Outcome: | The proposed framework uncovers systematic weaknesses obscured by standard metrics. |