PI: Dejing Dou
We propose to advance biomedical natural language processing by combining ontology-based reasoning, semantic search, and embedding-driven methods to improve information extraction, representation, and consistency detection in biomedical texts.
PI: 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.
PI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
PI: Dejing Dou
We propose to advance methods for semantic oppositeness detection in natural language, focusing on embedding-based models and contextual representations to improve reasoning and error detection in text.
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.
PI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.
Conference Papers
Imalsha Puranegedara, Themira Chathumina, Nisal Ranathunga, Nisansa de Silva, Surangika Ranathunga, and Mokanarangan Thayaparan, "Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages", in Moratuwa Engineering Research Conference (MERCon), 2025. doi: 10.1109/MERCon67903.2025.11216992 
Nisansa de Silva and Dejing Dou, "Semantic Oppositeness Embedding Using an Autoencoder-based Learning Model", in Database and Expert Systems Applications, 2019, pp. 159--174. doi: 10.1007/978-3-030-27615-7_12
Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, and Madhavi Perera, "Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity", in 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), 2017, pp. 1--6. doi: 10.1109/ICIINFS.2017.8300343
Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, and Buddhi Ayesha, "Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings", in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), 2017, pp. 79--84. doi: 10.1109/intech.2017.8102426
