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 Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition


C. Samarawickrama, M. de Almeida, N. de Silva, G. Ratnayaka, and A. Perera

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), IEEE, 2020, pp. 494--499,

In the field of natural language processing, domain-specific information retrieval using given documents has been a prominent and ongoing research area. Automatic extraction of the legal parties (petitioner and defendant sets) involved in a legal case has a significant impact on the proceedings of legal cases. This is a study proposing a novel way to identify the legal parties in a given legal document. The motivation behind this study is that there are no proper existing systems to accurately identify the legal parties in a legal document. We combined several existing natural language processing annotators to achieve the goal of extracting legal parties in a given court case document. Then, our methodology was evaluated with manually labelled court case paragraphs. The outcomes of the evaluation demonstrate that our system is successful in identifying legal parties.