Legal Party Extraction from Legal Opinion Text with Sequence to Sequence Learning
| Autor: | Melonie de Almeida, Amal Shehan Perera, Nisansa de Silva, Chamodi Samarawickrama, Gathika Ratnayaka |
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| Rok vydání: | 2020 |
| Předmět: |
Information retrieval
Computer science Specific-information 05 social sciences Sentiment analysis ComputingMilieux_LEGALASPECTSOFCOMPUTING 010501 environmental sciences 01 natural sciences Domain (software engineering) 0502 economics and business Legal opinion Task analysis Legal document Sequence learning 050207 economics Legal case 0105 earth and related environmental sciences |
| Zdroj: | 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer). |
| DOI: | 10.1109/icter51097.2020.9325488 |
| Popis: | In the field of natural language processing, domain specific information retrieval using given documents has been a prominent and ongoing research area. The automatic extraction of the legal parties involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to extract the legal parties involved in a given legal document. The motivation behind this study is that there is the absence of a proper automated system to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators together with a sequence to sequence learning model to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labeled court case sentences. The outcomes of the evaluation demonstrate that our system is successful in identifying legal parties. |
| Databáze: | OpenAIRE |
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