@inproceedings{wei-etal-2025-legalcore,
title = "{L}egal{C}ore: A Dataset for Event Coreference Resolution in Legal Documents",
author = "Wei, Kangda and
Shi, Xi and
Tong, Jonathan and
Sai Ramana Reddy and
Natarajan, Anandhavelu and
Jain, Rajiv and
Garimella, Aparna and
Huang, Ruihong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1284/",
doi = "10.18653/v1/2025.findings-acl.1284",
pages = "25044--25059",
ISBN = "979-8-89176-256-5",
abstract = "Recognizing events and their coreferential mentions in a document is essential for understanding semantic meanings of text. The existing research on event coreference resolution is mostly limited to news articles. In this paper, we present the first dataset for the legal domain, LegalCore, which has been annotated with comprehensive event and event coreference information. The legal contract documents we annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. The annotations show that legal documents have dense event mentions and feature both short-distance and super long-distance coreference links between event mentions. We further benchmark mainstream Large Language Models (LLMs) on this dataset for both event detection and event coreference resolution tasks, and find that this dataset poses significant challenges for state-of-the-art open-source and proprietary LLMs, which perform significantly worse than a supervised baseline. We will publish the dataset as well as the code."
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<abstract>Recognizing events and their coreferential mentions in a document is essential for understanding semantic meanings of text. The existing research on event coreference resolution is mostly limited to news articles. In this paper, we present the first dataset for the legal domain, LegalCore, which has been annotated with comprehensive event and event coreference information. The legal contract documents we annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. The annotations show that legal documents have dense event mentions and feature both short-distance and super long-distance coreference links between event mentions. We further benchmark mainstream Large Language Models (LLMs) on this dataset for both event detection and event coreference resolution tasks, and find that this dataset poses significant challenges for state-of-the-art open-source and proprietary LLMs, which perform significantly worse than a supervised baseline. We will publish the dataset as well as the code.</abstract>
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%0 Conference Proceedings
%T LegalCore: A Dataset for Event Coreference Resolution in Legal Documents
%A Wei, Kangda
%A Shi, Xi
%A Tong, Jonathan
%A Reddy, Sai Ramana
%A Natarajan, Anandhavelu
%A Jain, Rajiv
%A Garimella, Aparna
%A Huang, Ruihong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wei-etal-2025-legalcore
%X Recognizing events and their coreferential mentions in a document is essential for understanding semantic meanings of text. The existing research on event coreference resolution is mostly limited to news articles. In this paper, we present the first dataset for the legal domain, LegalCore, which has been annotated with comprehensive event and event coreference information. The legal contract documents we annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. The annotations show that legal documents have dense event mentions and feature both short-distance and super long-distance coreference links between event mentions. We further benchmark mainstream Large Language Models (LLMs) on this dataset for both event detection and event coreference resolution tasks, and find that this dataset poses significant challenges for state-of-the-art open-source and proprietary LLMs, which perform significantly worse than a supervised baseline. We will publish the dataset as well as the code.
%R 10.18653/v1/2025.findings-acl.1284
%U https://aclanthology.org/2025.findings-acl.1284/
%U https://doi.org/10.18653/v1/2025.findings-acl.1284
%P 25044-25059
Markdown (Informal)
[LegalCore: A Dataset for Event Coreference Resolution in Legal Documents](https://aclanthology.org/2025.findings-acl.1284/) (Wei et al., Findings 2025)
ACL
- Kangda Wei, Xi Shi, Jonathan Tong, Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, and Ruihong Huang. 2025. LegalCore: A Dataset for Event Coreference Resolution in Legal Documents. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25044–25059, Vienna, Austria. Association for Computational Linguistics.