@inproceedings{wang-etal-2022-d2gclf,
title = "{D}2{GCLF}: Document-to-Graph Classifier for Legal Document Classification",
author = "Wang, Qiqi and
Zhao, Kaiqi and
Amor, Robert and
Liu, Benjamin and
Wang, Ruofan",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.170",
doi = "10.18653/v1/2022.findings-naacl.170",
pages = "2208--2221",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T D2GCLF: Document-to-Graph Classifier for Legal Document Classification
%A Wang, Qiqi
%A Zhao, Kaiqi
%A Amor, Robert
%A Liu, Benjamin
%A Wang, Ruofan
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F wang-etal-2022-d2gclf
%X 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.
%R 10.18653/v1/2022.findings-naacl.170
%U https://aclanthology.org/2022.findings-naacl.170
%U https://doi.org/10.18653/v1/2022.findings-naacl.170
%P 2208-2221
Markdown (Informal)
[D2GCLF: Document-to-Graph Classifier for Legal Document Classification](https://aclanthology.org/2022.findings-naacl.170) (Wang et al., Findings 2022)
ACL