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
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
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
