BSc Eng. (Hons) (Moratuwa)
A dedicated and result-oriented Software Engineer equipped with technical abilities and decisive nature necessary for successful job development. Offering strong attention to detail and accuracy. Excellent problem-solving skills and the ability to work in fast-paced team environments.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law | Legal Domain |
Theses: BSc
Dilini Karunarathna, Chanika Mudalige, and Isanka Rajapaksha, "Party Based Sentiment Analysis of Legal Opinion Texts", University of Moratuwa, 2020
Journal Papers
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Sigmalaw PBSA-A Deep Learning Approach for Aspect-Based Sentiment Analysis in Legal Opinion Texts", Journal of Data Intelligence, vol. 3, no. 1, pp. 101--115, 2021. doi: 10.26421/JDI3.1-1
Conference Papers
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Amal Shehan Perera, and Gathika Ratnayaka, "Sigmalaw PBSA-A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain", in International Conference on Database and Expert Systems Applications, 2021, pp. 125--137. doi: 10.1007/978-3-030-86472-9_12 
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Rathnayaka, and Amal Shehan Perera, "Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts", in 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), 2020, pp. 284--285. doi: 10.1109/ICTer51097.2020.9325435
Chanika Ruchini Mudalige, Dilini Karunarathna, Isanka Rajapaksha, Nisansa de Silva, Gathika Ratnayaka, Amal Shehan Perera, and Ramesh Pathirana, "SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts", in 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020, pp. 488--493. doi: 10.1109/ICIIS51140.2020.9342650 
PI: Nisansa de Silva
We propose to advance aspect-based sentiment analysis (ABSA) by developing enhanced models, datasets, and evaluation methods that improve the accuracy and applicability of sentiment classification across multiple domains.
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.
