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

Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain


G. Weerasundara, and N. de Silva

arXiv preprint arXiv:2309.17171, 2023,

Many NLP tasks, although well-resolved for general English, face challenges in specific domains like fantasy literature. This is evident in Named Entity Recognition (NER), which detects and categorizes entities in text. We analyzed 10 NER models on 7 Dungeons and Dragons (D\&D) adventure books to assess domain-specific performance. Using open-source Large Language Models, we annotated named entities in these books and evaluated each model's precision. Our findings indicate that, without modifications, Flair, Trankit, and Spacy outperform others in identifying named entities in the D\&D context