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.