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

Proceedings of the 28th International Conference on Computational Linguistics, pages 1561–1571 Barcelona, Spain (Online), December 8-13, 2020 1561 Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement Yanguang Chen1, Yuanyuan Sun1∗, Zhihao Yang1, Hongfei Lin1 1School of Computer Science and Technology, Dalian University of Technology cygAriel@mail.dlut.edu.cn, {syuan, yangzh, hflin}@dlut.edu.cn 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 informa- tion 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 un- structured 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 entity 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 entity 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. 1 Introduction Automatic extraction of information from legal documents is crucial for legal document analysis and re- lated business processing. The techniques of information extraction are pivotal modules for down-stream judicial applications such as the assistance of reviewing case documents, identification of criminal case facts and auxiliary generation of legal documents. Besides, by implementing named entity recognition and relation extraction, the judgment documents can be transformed into several triplets, capturing entity pairs and their interrelations inside the fact description. The structured legal triplets conduce to legal knowledge graph construction, which benefits query capabilities and interpretability in judicial applica- tions. There are great quantities of actual judgment documents released on China Judgments Online1. The abundant information contained in these judgment documents is worthy of in-depth study thanks to the authenticity and typicality of the documents. In this work, we conduct information extraction based on these public legal texts. Recently, information extraction techniques have developed rapidly. Neural networks for named entity recognition (Lample et al., 2016; Zhu and Wang, 2019) and deep learning methods applying to relation extraction (Nguyen and Grishman, 2015; Zhou et al., 2016) are extensively investigated. Early methods accomplish the triplet extraction drawing on the idea of pipelining. They first recognize the entities in the texts and then classify each entity pair into predefined relation types. These methods suffer from the error propagation problem caused by the incorrect and redundant entities attained by the previous step. Besides, the interactions between the prediction of entities and relations also need to be emphasized. For these challenges, joint learning methods have been intensively carried out. This work is licensed under a Creative …

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