Year: 2025
Type: conference-paper
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 identification and event coreference resolution tasks, and find that this dataset poses significant challenges for both open-source and proprietary LLMs, which all perform significantly worse than a supervised baseline.Our data and code are available at https://github.com/
Authors: Kangda Wei, Xi Shi, Jonathan Tong, Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, Ruihong Huang
Institutions: Texas A&M University, Adobe Systems (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412944674