PI: Nisansa de Silva
The Foundational Data Science project investigates how systematic data acquisition, integration, analysis, modelling, and visualisation can be applied to real-world problems to generate reliable and actionable knowledge.
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: Rohan Samarajiva
We propose to investigate the role of natural language processing in government by building methods, frameworks, and resources that enable data-driven policy-making while ensuring ethical, transparent, and inclusive outcomes.
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
Nevidu Jayatilleke and Nisansa de Silva, "Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil", in Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing-Natural Language Processing in the Generative AI Era, 2025, pp. 471--480.
White Papers
Yudhanjaya Wijeratne, Nisansa de Silva, and Yashothara Shanmugarajah, "Natural Language Processing for Government: Problems and Potential", LIRNEasia, 2019. doi: 10.13140/RG.2.2.34297.31845
Preprints
Himath Dhanapala, Haren Daishika, Himandhi Kuruppu, Sithija Seneviratne, Ashini Kavindya, Patalee Narasinghe, Sandeepa Weerasekara, Nisansa de Silva, and Sandareka Wickramanayake, "Trilingual Topic Modeling of Sri Lankan Parliamentary Debates", arXiv preprint arXiv:2608.20365, 2026
