裁定文书中企业破产事件的自动化抽取

doi:10.3969/j.issn.1000-5641.201921015

华东师范大学学报（自然科学版） ›› 2020, Vol. 2020 ›› Issue (4): 88-97.doi: 10.3969/j.issn.1000-5641.201921015

裁定文书中企业破产事件的自动化抽取

杨佳乐, 王俊豪, 钱卫宁, 罗轶凤

华东师范大学数据科学与工程学院, 上海 200062

收稿日期:2019-08-26 发布日期:2020-07-20
通讯作者: 罗轶凤,男,副教授,硕士生导师,研究方向为文本数据挖掘与知识图谱.E-mail:yfluo@dase.ecnu.edu.cn E-mail:yfluo@dase.ecnu.edu.cn
基金资助:
国家重点研发计划(2018YFC0831900)

Automatic extraction of corporate bankruptcy-related events from ruling documents

YANG Jiale, WANG Junhao, QIAN Weining, LUO Yifeng

School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received:2019-08-26 Published:2020-07-20

摘要/Abstract

摘要： 提出了一种企业破产事件抽取框架, 该框架可以从法律裁定书等卷宗资料中检测出相应的法律事件, 并抽取出与事件相关的结构化要素信息. 该框架结合从法院所获得的裁定书等卷宗信息, 运用远程监督技术来构建模型训练数据; 再通过命名实体识别技术对句级别的文书进行序列标注; 最后结合自定义的事件触发词表与事件字典, 运用事件抽取技术对法律文书进行事件识别, 并给出对应事件的结构化信息. 实验结果表明本框架能够取得较高的事件识别精度, 是一种有效的企业破产事件抽取框架.

关键词: 企业破产, 命名实体识别, 事件抽取

Abstract: This paper proposes a framework for extracting corporate bankruptcy-related events from ruling documents and thus extracts structured information about the related events. Combined with ruling documents, our framework uses distant supervision to generate training data; applies named entity recognition techniques to implement sequence label tagging on sentences of litigation documents; and implements event extraction with a self-defined list of event trigger words as well as an event dictionary to detect bankruptcy-related events and gather structured information. Our experimental results demonstrate the effectiveness of the framework.

Key words: enterprise bankruptcy, named entity recognition, event extraction

中图分类号:

TP399

杨佳乐, 王俊豪, 钱卫宁, 罗轶凤. 裁定文书中企业破产事件的自动化抽取[J]. 华东师范大学学报（自然科学版）, 2020, 2020(4): 88-97.

YANG Jiale, WANG Junhao, QIAN Weining, LUO Yifeng. Automatic extraction of corporate bankruptcy-related events from ruling documents[J]. Journal of East China Normal University(Natural Science), 2020, 2020(4): 88-97.

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裁定文书中企业破产事件的自动化抽取

Automatic extraction of corporate bankruptcy-related events from ruling documents

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