Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement

Yanguang Chen, Yuanyuan Sun, Zhihao Yang, Hongfei Lin

Abstract
In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online. It is in great need of enabling machines to understand the semantic information stored in the documents which are transcribed in the form of natural language. The technique of information extraction provides a way of mining the valuable information implied in the unstructured judgment documents. We propose a Legal Triplet Extraction System for drug-related criminal judgment documents. The system extracts the entities and the semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. Furthermore, we manually annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain, which contributes to training supervised triplet extraction models and evaluating the model performance. Our experimental results show that the legal feature introduction and multi-task learning framework are feasible and effective for the Legal Triplet Extraction System. The F1 score of triplet extraction finally reaches 0.836 on the legal dataset.
Anthology ID:
2020.coling-main.137
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1561–1571
Language:
URL:
https://aclanthology.org/2020.coling-main.137
DOI:
10.18653/v1/2020.coling-main.137
Bibkey:
Cite (ACL):
Yanguang Chen, Yuanyuan Sun, Zhihao Yang, and Hongfei Lin. 2020. Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1561–1571, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (Chen et al., COLING 2020)
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