Papers with Named Entity Recognition
Chasing the Tail with Domain Generalization: A Case Study on Frequency-Enriched Datasets (2022.aacl-main)
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| Challenge: | In academic research, natural language understanding tasks are typically defined by creating annotated datasets in which each utterance is encountered once. |
| Approach: | They propose a method that explicitly uses utterance frequency in training data to learn models that are more robust to unknown distributions. |
| Outcome: | The proposed approach shows up to 7.02% relative improvement over baselines on the tail data. |
LENS: Learning Entities from Narratives of Skin Cancer (2025.coling-demos)
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| Challenge: | Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related forums. |
| Approach: | They propose to use reddit forums to create an automatic entity recognition system that can be used to predict skin cancer outcomes. |
| Outcome: | LENS achieves an overall entity-level F1 score of 0.561 . other notable results include “CANC_T” (0.747), “STG” (0.888), “POB” (0.914), “GENDER” (0.750), “A/G” (00.646), “EMO” (0.619), and “MHD” (0.503). |
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)
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| Challenge: | Existing studies treat named entity recognition as a sequential labeling problem. |
| Approach: | They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted . |
| Outcome: | The proposed framework outperforms competing models on four benchmark datasets. |
RACAI’s System at PharmaCoNER 2019 (D19-57)
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| Challenge: | RACAI researchers develop named entity recognition systems for Romanian language . current system is language-independent and can be improved by using language-dependent resources . |
| Approach: | They propose to train a named entity recognition system for Romanian language . they propose to use a gazetteer-based baseline and a RNN-based NER system . |
| Outcome: | The proposed system is language independent, provided language-dependent resources exist . the proposed system can detect entities with four labels: anatomical parts, disorders, medical procedures and chemical compounds . |
KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
| Challenge: | Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type. |
| Approach: | They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions. |
| Outcome: | The proposed tool improves the entity typing process by linking the candidate types with some practical functions. |
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition (2021.tacl-1)
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| Challenge: | Name Regularity Bias is a problem in NER models that use contextual information to predict the type of an ambiguous entity. |
| Approach: | They propose a model-agnostic training method that adds learnable adversarial noise to some entity mentions to improve their accuracy. |
| Outcome: | The proposed method outperforms feature-based models on name regularity bias . it adds learnable adversarial noise to some entity mentions, leading to gains . |
Adapting LLMs for Structured Natural Language API Integration (2024.emnlp-industry)
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Robin Chan, Katsiaryna Mirylenka, Thomas Gschwind, Christoph Miksovic, Paolo Scotton, Enrico Toniato, Abdel Labbi
| Challenge: | API integration is crucial for enterprise systems, but there are challenges in combining APIs based on user intent. |
| Approach: | They propose a framework that leverages large language models to integrate APIs based on natural language input. |
| Outcome: | The proposed framework improves performance over existing methods and RAGs based on open APIs . it can learn structural API constraints implicitly during training and retain structured knowledge . |
Value type: the bridge to a better DST model (2023.findings-acl)
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| Challenge: | Value type of the slots can provide lots of useful information for DST tasks. however, it has been ignored in most previous works. |
| Approach: | They propose a new framework for DST task based on slot value type . they propose to extract the type of token from each turn and train a Ner model to extract corresponding type-entity from each conversation according to the token. |
| Outcome: | The proposed framework is effective on two multi-domain task-oriented conversation datasets. |
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods to increase training data in low-resource domains may not be effective due to data scarcity. |
| Approach: | They propose a method to transform a high-resource domain into a low-resourced domain by changing its style-related attributes to generate synthetic data for training. |
| Outcome: | The proposed method can significantly improve results on five domain pairs under different data regimes. |
Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework (2025.naacl-long)
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| Challenge: | Recent studies have demonstrated that large language models (LLMs) can perform in named entity recognition tasks. |
| Approach: | They propose a framework for clinical named entity recognition that decomposes the entity recognition task into several retrievals of sub-types and then filters them. |
| Outcome: | The proposed framework improves on the clinical named entity recognition task. |
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. |
E-magyar – A Digital Language Processing System (L18-1)
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Tamás Váradi, Eszter Simon, Bálint Sass, Iván Mittelholcz, Attila Novák, Balázs Indig, Richárd Farkas, Veronika Vincze
| Challenge: | e-magyar is a free, open, modular text processing pipeline for Hungarian . existing tools were overhauled to operate in the pipeline with a uniform encoding and run in the same Java platform. |
| Approach: | e-magyar is a free, open, modular text processing pipeline for Hungarian . it was created by a collaborative effort by the language technology community . the system is aimed at a broad range of users, from language developers to researchers . |
| Outcome: | The proposed tool is open source and available for download on the HFST framework. |
Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge (2023.eacl-main)
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| Challenge: | Named entity recognition (NER) is costly because of lack of training data and domain experts. |
| Approach: | They propose a self-adaptive neural model that retrieves external knowledge from unstructured text to learn the usages of entities that have not been learned well. |
| Outcome: | The proposed model outperforms strong baselines on cross-neuro-ner datasets by 2.35 points in F1 metric. |
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models (2024.naacl-long)
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| Challenge: | Chemical named entity recognition (NER) models are used in many downstream tasks, but it is unknown whether they work the same for everyone. |
| Approach: | They develop a framework for measuring gender bias in chemical NER models . they analyze a corpus of 92,405 words with self-identified gender information from reddit . |
| Outcome: | The proposed framework measures gender bias in chemical NER models using synthetic data and a newly annotated corpus of over 92,405 words with self-identified gender information from Reddit. |
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. |
Towards Realistic Single-Task Continuous Learning Research for NER (2021.findings-emnlp)
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Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta
| Challenge: | Academic datasets are often static and contain data that is annotated all at once based on fixed annotation guidelines. |
| Approach: | They propose to build a single-task continuous learning dataset from an existing dataset and release it along with the code to the research community. |
| Outcome: | The proposed model is based on an existing dataset and released to the research community. |
De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention (2021.acl-long)
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| Challenge: | Existing methods for Named entity recognition (NER) rely on labeled data, which is labor-intensive. |
| Approach: | They propose a method to de-biase DS-NER models by a structural Causal Model . they propose to use a causal invariance regularizer to make them more robust . |
| Outcome: | The proposed method significantly improves DS-NER models on four datasets and three DS NER models. |
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. |
Spanish Datasets for Sensitive Entity Detection in the Legal Domain (2022.lrec-1)
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| Challenge: | The de-identification of sensible data is essential for data sharing and reuse, both for research and commercial purposes. |
| Approach: | They propose to use four datasets annotated for named entity detection in Spanish to fine-tune models for the task of named entity-detection. |
| Outcome: | The proposed model is based on four datasets annotated for named entity detection in Spanish with an estimated error rate of 14%. |
Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue (2023.emnlp-main)
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| Challenge: | Existing name entity recognition methods combine pre-trained language models with supervised models such as BiLSTM/LSTM-CRF to perform poorly in a spoken dialogue context. |
| Approach: | They propose a logic-guided fine-grained address recognition method that softly applies the logic rule to improve the accuracy of FGAER. |
| Outcome: | The proposed method improves fine-grained address entity recognition from multi-turn spoken dialogues. |
Enhancing Distantly Supervised Named Entity Recognition with Strong Label Guided Lottery Training (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) requires a limited quantity of strongly labeled data . weakly labeles can be acquired through distant supervision, but can cause noise . |
| Approach: | They propose a noise-robust learning framework where safe parameters can be identified . they conduct extensive experiments on multiple datasets and show it outperforms the state-of-the-art methods. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on weakly labeled data. |
Neural Architectures for Nested NER through Linearization (P19-1)
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| Challenge: | a nested named entity recognition (NER) is a set of entities that can overlap and be labeled with more than one label. |
| Approach: | They propose two neural network architectures for nested named entity recognition . they propose to model nesting entities as multilabels and predict a sequence-to-sequence problem . |
| Outcome: | The proposed methods outperform the state-of-the-art on four corpora . the proposed models also improve on the recently published contextual embeddings . |
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. |
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%. |
CASSI: Contextual and Semantic Structure-based Interpolation Augmentation for Low-Resource NER (2023.findings-emnlp)
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| Challenge: | Existing methods for text augmentation suffer from annotation corruption for token-level tasks like NER. |
| Approach: | They propose a novel augmentation scheme that generates high-quality contextually diverse augmentations while avoiding annotation corruption. |
| Outcome: | The proposed scheme outperforms existing methods at multiple low resource levels, in multiple languages, and for noisy and clean text. |
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. |
Soft Gazetteers for Low-Resource Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing named entity recognition models use gazetteers to improve performance, but they are limited in coverage and do not exist in low-resource languages. |
| Approach: | They propose a method that integrates Wikipedia information into named entity models by cross-lingual entity linking. |
| Outcome: | The proposed method improves on four low-resource languages with Wikipedia . it incorporates available information from english knowledge bases into neural models . |
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a key component of many text processing pipelines. |
| Approach: | They propose a new architecture tailored to the task of identifying named entities with data from multiple genres. |
| Outcome: | The proposed architecture outperforms baseline and competitive methods on all three setups with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. |
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
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| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
| Approach: | They propose a noise-robust learning scheme for training named entity recognition models using only distantly-labeled data and a self-training method that uses contextualized augmentations created by pre-trained language models. |
| Outcome: | The proposed method outperforms existing supervised NER models on three datasets by significant margins. |
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. |
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
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| Challenge: | Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries. |
| Approach: | They propose a noise-robust learning approach that learns from data with partially incorrect labels. |
| Outcome: | The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation. |