Nisansa Dilushan de Silva

I am a lecturer at the Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka. I obtained my Ph.D (2020, advisor: Dejing Dou) and MS (2016) degrees in Computer and Information Science from University of Oregon, USA and my BSc (Hons) in Computer Science & Engineering (2011) degree from University of Moratuwa. I joined University of Moratuwa as a lecturer in 2011. From 2013 to 2014, I worked as a researcher at LIRNEasia. In 2018, I worked as a Givens Associate at Argonne National Laboratory, USA. I have over 80 peer reviewed publications in the field of computer science, mostly under the subfields of Natural Language Processing and Artificial Intelligence, earning more than 1400 citations. I am also an associate member of the IESL.

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

Party-based Sentiment Analysis Pipeline for the Legal Domain


S. Jayasinghe, L. Rambukkanage, A. Silva, N. de Silva, and A. Perera

2021 21st International Conference on Advances in ICT for Emerging Regions (ICter), 2021, pp. 171-176,

Since the advent of research to automate existing manual language workflows, legal domain has risen as a key area. When it comes to making the process of court cases more efficient with the said automation, legal information extraction is vital for legal professionals. In this study, we propose a unified pipeline to annotate party-based sentiment for court cases which reduces manual work. To achieve this end, we combined two state-of-the-art models into a single workflow. The first model extracts the entities which represent each party in a court case, while the second model analyzes the sentiment with respect to each party in a given sentence of a court case. In this study we propose two approaches for defining the pipeline which maps the output of the party extraction system into the party-based sentiment analysis system. Further, we propose improvements to the existing party extraction system.