Challenge: Recent papers introduce methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models.
Approach: They propose to integrate gazetteer features and entity segmentation techniques into neural named entity recognition models.
Outcome: The proposed methods improve entity segmentation and not just entity typing.

Similar Papers

Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)

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Challenge: Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features.
Approach: They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities.
Outcome: The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features.
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 .
A Neural Multi-digraph Model for Chinese NER with Gazetteers (P19-1)

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Challenge: Existing approaches to incorporating gazetteers into NER systems rely on manually defined selection strategies or handcrafted templates, which may not lead to optimal effectiveness.
Approach: They propose to use graph neural networks to automatically learn how to incorporate multiple gazetteers into an NER system by capturing the information that the gazetteer offers.
Outcome: The proposed model outperforms existing methods on Chinese NER datasets while incorporating rich gazetteer information while resolving ambiguities.
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.
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 .
GPT-NER: Named Entity Recognition via Large Language Models (2025.findings-naacl)

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Challenge: Large-scale language models (LLMs) have shown impressive ability for in-context learning with limited training data.
Approach: They propose a novel sequence labeling task that transforms a sequence labeled as a text-generation task into a self-verification task that LLMs can adapt to.
Outcome: The proposed model performs better on NER than supervised models on a variety of tasks . the proposed model can be easily adapted by LLMs to generate a text sequence .
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)

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Challenge: Named entity recognition (NER) is a fundamental NLP task.
Approach: They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers .
Outcome: The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark.
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.
Better Feature Integration for Named Entity Recognition (2021.naacl-main)

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Challenge: Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant.
Approach: They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact.
Outcome: The proposed model achieves better performance than previous approaches while requiring fewer parameters.
Robust Lexical Features for Improved Neural Network Named-Entity Recognition (C18-1)

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Challenge: Named-Entity Recognition (NER) uses word embeddings to extend, rather than replace, hand-crafted features.
Approach: They propose to embed words and entity types into a low-dimensional vector space and compute a feature vector representing each word offline.
Outcome: The proposed representations outperform existing models and achieve state-of-the-art performance.

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