# LegalCore: A Dataset for Event Coreference Resolution in Legal Documents

arXiv: https://arxiv.org/abs/2502.12509 · HTML: https://mlsanity.com/paper/2502.12509/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing](https://mlsanity.com/paper/2406.16253/) (2024)
- [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://mlsanity.com/paper/1707.07344/) (2017)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs](https://mlsanity.com/paper/2401.17922/) (2024)
- [Are LLMs Good Annotators for Discourse-level Event Relation Extraction?](https://mlsanity.com/paper/2407.19568/) (2024)
- [Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers](https://mlsanity.com/paper/2311.07538/) (2023)

## Built on by (most cited first, 4 of 4)

- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.16984/) (2026)
- [LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence](https://mlsanity.com/paper/2610.01027/) (2026)
- [CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation](https://mlsanity.com/paper/2610.12580/) (2026)

## Abstract (arXiv)

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.