SigmaLaw
PI: Nisansa de Silva
This project delves into the synergy between Natural Language Processing (NLP) and the legal domain, presenting novel solutions to enduring challenges.
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Mr. Thejan Rupasinghe
Technical Lead - Data Engineering at Sysco LABS
BSc Eng. (Hons) (Moratuwa)
I'm interested in working with new technologies and meeting new people. Being a hard-working team player, I always will contribute my best to achieve the team goal. I'm eager to get involved with challenging and innovative projects which will utilize my skills.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Ontologies | Law |
Publications
Theses: BSc
Gathika Ratnayaka, Thejan Rupasinghe, Menuka Warushavithana, and Viraj Gamage, "Ontology-Based Information Extraction for Automatic Generation of Legal Arguments", University of Moratuwa, 2019
Journal Papers
Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Gamage, Madhavi Perera, and Amal Shehan Perera, "Classifying Sentences in Court Case Transcripts using Discourse and Argumentative Properties", ICTer, vol. 12, no. 1, 2019. doi: 10.4038/icter.v12i1.7200
Conference Papers
Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Gamage, and Amal Shehan Perera, "Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations", in 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), 2018, pp. 13--20. doi: 10.1109/ICTER.2018.8615485
Workshop Papers
Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Viraj Gamage, Menuka Warushavithana, and Amal Shehan Perera, "Shift-of-Perspective Identification Within Legal Cases", in Proceedings of the 3rd Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts, 2019.
Viraj Gamage, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, and Thejan Rupasinghe, "Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning", in Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, 2018, pp. 260-265. doi: 10.18653/v1/W18-6238
Projects