Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition

Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, A. Perera
{"title":"Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition","authors":"Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, A. Perera","doi":"10.1109/ICIIS51140.2020.9342720","DOIUrl":null,"url":null,"abstract":"In the field of natural language processing, domain-specific information retrieval using given documents has been a prominent and ongoing research area. Automatic extraction of the legal parties (petitioner and defendant sets) involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to identify the legal parties in a given legal document. The motivation behind this study is that there are no proper existing systems to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labelled court case paragraphs. The outcomes of the evaluation demonstrate that our system is successful in identifying legal parties.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIIS51140.2020.9342720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In the field of natural language processing, domain-specific information retrieval using given documents has been a prominent and ongoing research area. Automatic extraction of the legal parties (petitioner and defendant sets) involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to identify the legal parties in a given legal document. The motivation behind this study is that there are no proper existing systems to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labelled court case paragraphs. The outcomes of the evaluation demonstrate that our system is successful in identifying legal parties.
使用共同参考决议和命名实体识别的法律文件的当事人识别
在自然语言处理领域中,基于给定文档的特定领域信息检索一直是一个重要的研究方向。法律案件当事人(原告和被告)的自动提取对法律案件的诉讼程序有重大影响。这是一项研究,提出了一种识别给定法律文件中法律当事人的新方法。这项研究背后的动机是,没有适当的现有制度来准确识别法律文件中的法律当事人。我们结合了几个现有的自然语言处理注释器,以实现在给定的法庭案件文档中提取法律当事人的目标。然后,用人工标记的法庭案例段落评估我们的方法。评估结果表明,我们的制度在识别法律当事人方面是成功的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信