| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
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Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy (2025.coling-main)
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| Challenge: | Existing models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), which hinders the achievement of satisfactory performance. |
| Approach: | They propose a framework which fully leverages sentence-level information to improve OOE-NER performance by exploiting pre-trained language models' ability to understand target entity’s sentence context with a template set and refines sentence representation based on positive and negative templates. |
| Outcome: | The proposed framework outperforms state-of-the-art models on five datasets on named entity recognition (NER) tasks. |
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 . |
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. |
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. |
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning (P19-1)
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| Challenge: | Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. |
| Approach: | They propose a method to perform named entity recognition using unlabeled data and named entity dictionaries. |
| Outcome: | The proposed method can estimate task loss as if there is fully labeled data. |
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. |
SetGNER: General Named Entity Recognition as Entity Set Generation (2022.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a fundamental task in the field of information extraction and has played an important role in the development of natural language processing. |
| Approach: | They propose a method that treats each entity as a sequence and is capable of recognizing discontinuous mentions. |
| Outcome: | The proposed model outperforms state-of-the-art generative NER models on two discontinuous NER datasets, two nested NER and one flat NER. |
Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)
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| Challenge: | Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text. |
| Approach: | They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation. |
| Outcome: | The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities. |