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: 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.
PI: Nisansa de Silva
We propose to advance automatic summarization and generation techniques, with a focus on aspect-aware methods that capture fine-grained information and sentiment within concise textual representations.
Theses: MSc Minor Component Research
Pubudu Cooray, "Headline Generation for Sinhala Newspaper Articles using Pre-trained Language Models", University of Moratuwa, 2025
Dushan Kumarasinghe, "Automatic Generation of Research Paper Abstracts using Deep-Hybrid Models", University of Moratuwa, 2025
Kushan Hewapathirana, "Towards Multi-document Summarisation in Low-resource Settings", University of Moratuwa, 2025
Conference Papers
K M Hewapathirana, Nisansa de Silva, C D Athuraliya, and Piumi Kandanaarachchi, "Domain Adaptation for Multi-document Summarisation: A Case Study in the Medical Research Domain", in Proceedings of the 39th Pacific Asia Conference on Language, Information and Computation, 2025, pp. 791--802.
Dushan Kumarasinghe and Nisansa de Silva, "Abstract Generation with Hybrid Model Supported by Relevance Matrix", in International Conference on Computational Collective Intelligence, 2024, pp. 211--223. doi: 10.1007/978-3-031-70816-9_17
Dushan Kumarasinghe and Nisansa de Silva, "Automatic Generation of Abstracts for Research Papers", in Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), 2022, pp. 221--229.
