Lexical Features in Coreference Resolution: To be Used With Caution
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Abstract (arXiv)
Lexical features are a major source of information in state-of-the-art coreference resolvers. Lexical features implicitly model some of the linguistic phenomena at a fine granularity level. They are especially useful for representing the context of mentions. In this paper we investigate a drawback of using many lexical features in state-of-the-art coreference resolvers. We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains. Furthermore, we show that the current coreference resolution evaluation is clearly flawed by only evaluating on a specific split of a specific dataset in which there is a notable overlap between the training, development and test sets.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2017.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 4; 2024: 2; 2025: 1.
12 papers.
- 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 98
- 2023GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluationcited by 19
- 2021OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genrescited by 18
- 2023Are Large Language Models Robust Coreference Resolvers?cited by 15
- 2024Major Entity Identification: A Generalizable Alternative to Coreference Resolutioncited by 9
- 2021Automatic Fake News Detection: Are Models Learning to Reason?cited by 7
- 2023Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspectivecited by 6
- 2024GUMsley: Evaluating Entity Salience in Summarization for 12 English Genrescited by 6
- 2021Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithmscited by 3
- 2021Separating Retention from Extraction in the Evaluation of End-to-end Relation Extractioncited by 2
- 2023Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalizationcited by 1
- 2025Mah=an=ama: A Unique Testbed for Literary Entity Discovery and Linkingcited by 0
- 2025Mah=an=ama: A Unique Testbed for Literary Entity Discovery and Linking24 Sep 2025
- 2024Major Entity Identification: A Generalizable Alternative to Coreference Resolution20 Jun 2024
- 2024GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres31 Jan 2024
- 2023Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization20 Sep 2023
- 2023GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation3 Jun 2023
- 2023Are Large Language Models Robust Coreference Resolvers?23 May 2023
- 2023Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective16 Mar 2023
- 2022State-of-the-art generalisation research in NLP: A taxonomy and review6 Oct 2022
- 2021Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms12 Oct 2021
- 2021Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction24 Sep 2021
- 2021OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres2 Jun 2021
- 2021Automatic Fake News Detection: Are Models Learning to Reason?17 May 2021