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

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

doi: 10.3969/j.issn.1000-5641.201921015

华东师范大学数据科学与工程学院, 上海　200062

基金项目: 国家重点研发计划(2018YFC0831900)

详细信息

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

中图分类号: TP399
计量
- 文章访问数: 202
- HTML全文浏览量: 177
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2019-08-26
- 网络出版日期: 2020-07-20
- 刊出日期: 2020-07-25

Automatic extraction of corporate bankruptcy-related events from ruling documents

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

摘要

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.
- 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.
- enterprise bankruptcy /
- named entity recognition /
- event extraction

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图 1 法律事件要素抽取整体框架图

Fig. 1 Overview of the legal event key-argument extraction framework

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图 2 标签数据的生成过程

Fig. 2 The labeled data generation process

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图 3 Bi-LSTM + CRF模型图

Fig. 3 The Bi-LSTM + CRF Model

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图 4 事件抽取框架

Fig. 4 The architecture of event extraction

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图 5 NER标注示例

Fig. 5 Tagging example of named entity recognition

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表 1 远程监督下自动标注数据统计

Tab. 1 Statistics of automatically labeled data via distant supervision

数据集	远程监督	正例数目	负例数目
借贷纠纷	1 956	1 980	3 144
劳务纠纷	1 523	1 565	4 142
总计	3 507	3 545	7 286

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表 2 NER评测结果

Tab. 2 Precision, recall, and F1 score of named entity recognition

实体类型	准确率	召回率	F1值
LOC	0.557	0.796	0.655
TIME	0.599	0.917	0.725
PER	0.825	0.803	0.814
ORG	0.839	0.797	0.818

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表 3 劳务纠纷的20个子事件

Tab. 3 20 Sub-events of labor event

劳务纠纷	标签	劳务纠纷	标签
解除劳动关系	LB1	法院驳回仲裁申请	LB11
支付工资报酬	LB2	解除担保	LB12
经济补偿款	LB3	停产、裁员	LB13
拖欠工资、报酬	LB4	奖金	LB14
存在/确认劳动关系	LB5	工衣费	LB15
双倍工资差额	LB6	工作岗位	LB16
签订劳动合同期限	LB7	死亡抚恤待遇	LB17
带薪休假、加班	LB8	解除合同通知书	LB18
未签订劳动合同	LB9	吊销营业执照	LB19
工伤、补助费	LB10	达成/调解协议	LB20

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表 4 事件抽取评测结果

Tab. 4 Precision, recall, and F1 score of event extraction

事件类型	准确率	召回率	F1值
劳务纠纷	0.843	0.872	0.859
借贷纠纷	0.815	0.847	0.831
平均值	0.829	0.859	0.845

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