Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations

TL;DR

This paper presents a novel method for classifying discourse relationships between sentences in US court case transcripts, combining machine learning and rule-based approaches, to enhance legal text analysis.

Contribution

It introduces the first system to identify discourse relations in legal transcripts, improving understanding of legal case documents.

Findings

System achieved high accuracy in classifying sentence relationships.

Human judges validated the effectiveness of the approach.

First application of discourse analysis to legal transcripts.

Abstract

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.