Professional Experience

  • Present 2020

    Senior Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2021 2020

    Research Fellow

    LIRNEasia,
    Sri Lanka

  • 2020 2014

    Graduate Research/Teaching Fellow

    University of Oregon, Department of Computer and Information Science,
    USA.

  • 2018 2018

    Givens Associate

    Argonne National Laboratory,
    USA.

  • 2020 2011

    Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2014 2013

    Researcher

    LIRNEasia,
    Sri Lanka

  • 2014 2013

    Visiting Lecturer

    Northshore College of Business and Technology,
    Sri Lanka

Education

  • Ph.D. 2020

    Ph.D. in Computer & Information Science

    University of Oregon, USA

  • MS 2016

    MS in Computer & Information Science

    University of Oregon, USA

  • BSc2011

    B.Sc Engineering (Hons)in Computer Science & Engineering

    University of Moratuwa, Sri Lanka

Featured Research

Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations


G. Ratnayaka, T. Rupasinghe, N. de Silva, M. Warushavithana, V. Gamage, and A. Perera

2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE, 2018, pp. 13--20,

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.