Classifying Sentences in Court Case Transcripts using Discourse and Argumentative Properties

Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Salaka Gamage, Madhavi Perera, Amal Shehan Perera


Information that is available in court case transcripts which describes the proceedings of previous legal cases are of significant importance to legal officials. Therefore, automatic information extraction from court case transcripts can be considered as a task of huge importance when it comes to facilitating the processes related to the legal domain. A sentence can be considered as a fundamental textual unit of any document which is made up of text. Therefore, analyzing the properties of sentences can be of immense value when it comes to information extraction from machine-readable text. This paper demonstrates how the properties of sentences can be used to extract valuable information from court case transcripts. As the first task, the sentence pairs were classified based on the relationship type which can be observed between the two sentences. There, we defined relationship types that can be observed between sentences in court case transcripts. A system combining a machine learning model and a rule-based approach was used to classify pairs of sentences according to the relationship type. The next classification task was performed based on whether a given sentence provides a legal argument or not. The results obtained through the proposed methodologies 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. Similarly, this study provides novel and effective approaches to identify argumentative sentences in court case transcripts.

Full Text:


Printing Sponsor
University of Colombo
School of Computing

Managed & Published

Creative Commons License
This journal is published under a Creative Commons Attribution 4.0 International License.