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 .

Similar Papers

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

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Challenge: Existing models for named entity recognition have limited range when applied to long documents . lack of supervision for such a task means one has to settle for unsupervised approaches.
Approach: They propose to train a neural context retrieval model based on an instruction-tuned large language model.
Outcome: The proposed method outperforms baselines on an English literary dataset . pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, but limited range when applied to long documents such as whole novels.
Attending to Long-Distance Document Context for Sequence Labeling (2020.findings-emnlp)

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Challenge: UC Berkeley researchers develop a method for incorporating global context in long documents . many of the main datasets used in NLP are comprised of relatively short documents - english OntoNotes contains 223 tokens .
Approach: They propose a method for incorporating global context in long documents . they use multiple mentions of the same word type to generate a representation for each token .
Outcome: The proposed model performs better at recognizing entities with high TF-IDF scores than parametric models lacking context.
Do “English” Named Entity Recognizers Work Well on Global Englishes? (2023.findings-emnlp)

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Challenge: Most of English named entity recognition datasets contain American or British English data . multiple problems may occur in low-resource English contexts, such as confusion of named entities with regionspecific meanings .
Approach: They build a newswire dataset to analyze NER model performance on low-resource English variants . they find that models trained on the CoNLL or OntoNotes datasets experienced significant performance drops .
Outcome: The results show that models trained on the CoNLL or OntoNotes datasets experienced significant performance drops.
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 .
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.
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.
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.
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.
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.
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)

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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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