Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
| Autor: | Menuka Warushavithana, Amal Shehan Perera, Nisansa de Silva, Gathika Ratnayaka, Viraj Gamage, Thejan Rupasinghe |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language Common law Machine Learning (stat.ML) 06 humanities and the arts 02 engineering and technology 0603 philosophy ethics and religion Legal domain Linguistics Machine Learning (cs.LG) Support vector machine Court case Statistics - Machine Learning Relationship Type 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 060301 applied ethics Psychology Computation and Language (cs.CL) Mechanism (sociology) |
| DOI: | 10.48550/arxiv.1809.03416 |
| Popis: | Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts. Comment: Conference: 2018 International Conference on Advances in ICT for Emerging Regions (ICTer) |
| Databáze: | OpenAIRE |
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