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

Unified Legal Party Based Sentiment Analysis Pipeline


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

The International Journal on Advances in ICT for Emerging Regions, vol. 15, no. 2, 2022,

The rapid growth of text corpora across various domains has emerged a need and an opportunity to leverage Natural Language Processing to automate and efficiently streamline tedious manual tasks. Legal domain is one such text rich domain which suffers a rapid growth of text corpora and requirement for natural language processing applications. In the pursuit of automating the prediction of the winning party of a court case among other usages, analysing sentiment in a party wise manner is beneficial for legal professionals. The two main sub-tasks in this process is to identify parties in a court case and afterwards analysing the respective sentiment towards each party. In this study we discuss the unification of two such models capable of doing the two task into a single pipeline to perform party based sentiment analysis efficiently.