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: 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: 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
This project delves into the synergy between Natural Language Processing (NLP) and the legal domain, presenting novel solutions to enduring challenges.
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
Fernando Gutierrez, Dejing Dou, Nisansa de Silva, and Stephen Fickas, "Online Reasoning for Semantic Error Detection in Text", Journal on Data Semantics, Jul. 2017. doi: 10.1007/s13740-017-0079-6
Conference Papers
Sahan Jayasinghe, Lakith Rambukkanage, Ashan Silva, Nisansa de Silva, and Amal Shehan Perera, "Party-based Sentiment Analysis Pipeline for the Legal Domain", in 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter), 2021, pp. 171-176. doi: 10.1109/ICter53630.2021.9774821 
Sahan Jayasinghe, Lakith Rambukkanage, Ashan Silva, Nisansa de Silva, and Amal Shehan Perera, "Critical Sentence Identification in Legal Cases Using Multi-Class Classification", in 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS), 2021, pp. 146--151. doi: 10.1109/ICIIS53135.2021.9660657
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
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 
Nisansa de Silva, Dejing Dou, and Jingshan Huang, "Discovering Inconsistencies in PubMed Abstracts Through Ontology-Based Information Extraction", in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, New York, NY, USA: ACM, 2017, pp. 362--371. doi: 10.1145/3107411.3107452
Workshop Papers
Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Viraj Gamage, Menuka Warushavithana, and Amal Shehan Perera, "Shift-of-Perspective Identification Within Legal Cases", in Proceedings of the 3rd Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts, 2019.
