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
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.
PI: Nisansa de Silva
We propose to advance social media text analysis by combining linguistic study, preprocessing, and deep learning models to better capture sentiment, structure, and meaning in informal, large-scale user-generated text.
Journal Papers
W M Yomal De Mel and Nisansa de Silva, "Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study", ICTer, vol. 18, no. 2, pp. 121-130, 2025. doi: 10.4038/icter.v18i2.7299
Pengwei Wang, Lei Ji, Jun Yan, Dejing Dou, Nisansa de Silva, Yong Zhang, and Lianwen Jin, "Concept and Attention-Based CNN for Question Retrieval in Multi-View Learning", ACM Transactions on Intelligent Systems and Technology (TIST), vol. 9, no. 4, pp. 41, 2018. doi: 10.1145/3151957
Conference Papers
Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, and Madhavi Perera, "Legal Document Retrieval using Document Vector Embeddings and Deep Learning", in Science and information conference, 2018, pp. 160--175. doi: 10.1007/978-3-030-01177-2_12
