MSc (Moratuwa), BSc Eng. (Hons) (Moratuwa)
Final-year PhD candidate at the Australian National University (ANU), advancing the frontiers of graph matching through novel deep learning techniques. An enthusiastic researcher with expertise in deep learning, optimal transport, and algorithmic innovations. Previously worked as an R&D engineer, delivering high-impact solutions in domain drop catching and edge computing.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law | Ontologies | Sentiment Analysis | Legal Domain | Information Extraction | Named Entity Recognition | Coreference Resolution | Legal Party Identification | Semantic Analysis | Legal Information Extraction |
Theses: MSc Major Component Research
Gathika Ratnayaka, "Minimizing domain bias when adapting sentiment analysis techniques to the legal domain", University of Moratuwa, 2022
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, Nisansa de Silva, Amal Shehan Perera, Gayan Kavirathne, Thirasara Ariyarathna, and Anjana Wijesinghe, "Context Sensitive Verb Similarity Dataset for Legal Information Extraction", Data, vol. 7, no. 7, 2022. doi: 10.3390/data7070087
Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Legal Party Extraction from Legal Opinion Texts Using Recurrent Deep Neural Networks", Journal of Data Intelligence, vol. 3, no. 3, pp. 350--365, 2022. doi: 10.26421/JDI3.3-4
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Sigmalaw PBSA-A Deep Learning Approach for Aspect-Based Sentiment Analysis in Legal Opinion Texts", Journal of Data Intelligence, vol. 3, no. 1, pp. 101--115, 2021. doi: 10.26421/JDI3.1-1
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
Melonie de Almeida, Chamodi Samarawickrama, Nisansa de Silva, Gathika Ratnayaka, and Shehan Perera, "Identifying Legal Party Members from Legal Opinion Documents using Natural Language Processing", in The 23rd International Conference on Information Integration and Web Intelligence, 2021, pp. 259--266. doi: 10.1145/3487664.3487700 
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Amal Shehan Perera, and Gathika Ratnayaka, "Sigmalaw PBSA-A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain", in International Conference on Database and Expert Systems Applications, 2021, pp. 125--137. doi: 10.1007/978-3-030-86472-9_12
Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition", in 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020, pp. 494--499. doi: 10.1109/ICIIS51140.2020.9342720
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Rathnayaka, and Amal Shehan Perera, "Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts", in 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), 2020, pp. 284--285. doi: 10.1109/ICTer51097.2020.9325435
Melonie de Almeida, Chamodi Samarawickrama, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Legal Party Extraction from Legal Opinion Text with Sequence to Sequence Learning", in 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), 2020, pp. 143--148. doi: 10.1109/ICTer51097.2020.9325488
Chanika Ruchini Mudalige, Dilini Karunarathna, Isanka Rajapaksha, Nisansa de Silva, Gathika Ratnayaka, Amal Shehan Perera, and Ramesh Pathirana, "SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts", in 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020, pp. 488--493. doi: 10.1109/ICIIS51140.2020.9342650 
Gathika Ratnayaka, Nisansa de Silva, Amal Shehan Perera, and Ramesh Pathirana, "Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting", in Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation, 2020, pp. 252--260.
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
ActivePI: Nisansa de Silva
We propose to advance aspect-based sentiment analysis (ABSA) by developing enhanced models, datasets, and evaluation methods that improve the accuracy and applicability of sentiment classification across multiple domains.
ActivePI: Nisansa de Silva
The Knowledge Representation project investigates methods for structuring, aligning, and reasoning over knowledge extracted from language, with particular emphasis on low-resource settings, domain-specific semantics, and intelligent information access.
ActivePI: Nisansa de Silva
This project delves into the synergy between Natural Language Processing (NLP) and the legal domain, presenting novel solutions to enduring challenges.
