Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
This work addresses the need for lawyers and legal officials to extract valuable information from previous court cases, though it appears incremental as it adapts existing discourse relation methods to a new legal domain.
The paper tackled the problem of identifying relationships among sentences in U.S. court case transcripts by applying discourse relations, using a combined machine learning and rule-based approach to classify sentence pairs, with results evaluated by human judges.
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.