D2GCLF: Document-to-Graph Classifier for Legal Document Classification

Qiqi Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, Ruofan Wang


Abstract
Legal document classification is an essential task in law intelligence to automate the labor-intensive law case filing process. Unlike traditional document classification problems, legal documents should be classified by reasons and facts instead of topics. We propose a Document-to-Graph Classifier (D2GCLF), which extracts facts as relations between key participants in the law case and represents a legal document with four relation graphs. Each graph is responsible for capturing different relations between the litigation participants. We further develop a graph attention network on top of the four relation graphs to classify the legal documents. Experiments on a real-world legal document dataset show that D2GCLF outperforms the state-of-the-art methods in terms of accuracy.
Anthology ID:
2022.findings-naacl.170
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2208–2221
Language:
URL:
https://aclanthology.org/2022.findings-naacl.170
DOI:
10.18653/v1/2022.findings-naacl.170
Bibkey:
Cite (ACL):
Qiqi Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, and Ruofan Wang. 2022. D2GCLF: Document-to-Graph Classifier for Legal Document Classification. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2208–2221, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
D2GCLF: Document-to-Graph Classifier for Legal Document Classification (Wang et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-naacl.170.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.170.mp4