SigmaLaw
Principal Investigator: Nisansa de Silva
This project delves into the synergy between Natural Language Processing (NLP) and the legal domain, presenting novel solutions to enduring challenges.
Amidst the surge in textual data across industries, NLP emerges as a transformative tool, automating tasks and enriching insights. Within the legal landscape, characterized by abundant text resources, these studies navigate the intricacies of legal information extraction, sentiment analysis, and semantic comprehension. Fusing domain expertise, semantic understanding, and inventive methodologies, these studies unveil NLP's latent potential within the legal realm, paving a path towards efficiency and profound insights in legal practice and analysis. The amalgamation of NLP and the legal domain is poised to reshape traditional legal practices and empower legal professionals with cutting-edge tools.
Objectives:
- Explore the synergy between Natural Language Processing (NLP) and legal discourse
- Pioneer domain-specific sentiment analysis tailored for the legal field
- Address the challenge of domain-specific information retrieval in the legal domain
- Develop a novel deep learning framework for the legal domain
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law | Ontologies | Big Data | Legal Domain | Information Extraction | Legal Party Identification | Aspect-based Sentiment Analysis | Word2vec | Coreference Resolution | Named Entity Recognition | Word Embedding | Semantic Similarity | Sentiment Analysis | Legal Information Extraction | Neural Networks |
Publications
Journal Papers
Sahan Jayasinghe, Lakith Ransika Rambukkanage, Ashan Silva, Nisansa de Silva, and Amal Shehan Perera, "Unified Legal Party Based Sentiment Analysis Pipeline", The International Journal on Advances in ICT for Emerging Regions, vol. 15, no. 2, 2022. doi: 10.4038/icter.v15i2.7247
Gathika Ratnayaka, Nisansa de Silva, Amal Shehan Perera, Gayan Kavirathne, Thirasara Ariyarathna, and Anjana Wijesinghe, "Context Sensitive Verb Similarity Dataset for Legal Information Extraction", Data, vol. 7, no. 7, 2022. doi: 10.3390/data7070087
Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Legal Party Extraction from Legal Opinion Texts Using Recurrent Deep Neural Networks", Journal of Data Intelligence, vol. 3, no. 3, pp. 350--365, 2022. doi: 10.26421/JDI3.3-4
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
Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Gamage, Madhavi Perera, and Amal Shehan Perera, "Classifying Sentences in Court Case Transcripts using Discourse and Argumentative Properties", ICTer, vol. 12, no. 1, 2019. doi: 10.4038/icter.v12i1.7200
Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha, and Madhavi Perera, "Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population", International Journal on Advances in ICT for Emerging Regions, vol. 10, no. 1, pp. 1, 2017. doi: 10.4038/icter.v11i1.7191
Conference Papers
Sahan Jayasinghe, Lakith Rambukkanage, Ashan Silva, Nisansa de Silva, Shehan Perera, and Madhavi Perera, "Learning Sentence Embeddings in the Legal Domain with Low Resource Settings", in Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation, 2022, pp. 494--502.
Sahan Jayasinghe, Lakith Rambukkanage, Ashan Silva, Nisansa de Silva, and Amal Shehan Perera, "Legal Case Winning Party Prediction With Domain Specific Auxiliary Models", in Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), 2022, pp. 205--213.
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
Melonie de Almeida, Chamodi Samarawickrama, Nisansa de Silva, Gathika Ratnayaka, and Shehan Perera, "Identifying Legal Party Members from Legal Opinion Documents using Natural Language Processing", in The 23rd International Conference on Information Integration and Web Intelligence, 2021, pp. 259--266. doi: 10.1145/3487664.3487700
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
Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition", in 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020, pp. 494--499. doi: 10.1109/ICIIS51140.2020.9342720
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
Melonie de Almeida, Chamodi Samarawickrama, Nisansa de Silva, Gathika Ratnayaka, and Amal Shehan Perera, "Legal Party Extraction from Legal Opinion Text with Sequence to Sequence Learning", in 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), 2020, pp. 143--148. doi: 10.1109/ICTer51097.2020.9325488
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
Gathika Ratnayaka, Nisansa de Silva, Amal Shehan Perera, and Ramesh Pathirana, "Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting", in Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation, 2020, pp. 252--260.
Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Gamage, and Amal Shehan Perera, "Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations", in 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), 2018, pp. 13--20. doi: 10.1109/ICTER.2018.8615485
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
Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, and Madhavi Perera, "Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity", in 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), 2017, pp. 1--6. doi: 10.1109/ICIINFS.2017.8300343
Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha, and Madhavi Perera, "Semi-Supervised Instance Population of an Ontology using Word Vector Embedding", in 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), 2017, pp. 1--7. doi: 10.1109/icter.2017.8257822
Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, and Buddhi Ayesha, "Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings", in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), 2017, pp. 79--84. doi: 10.1109/intech.2017.8102426
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.
Viraj Gamage, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, and Thejan Rupasinghe, "Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning", in Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, 2018, pp. 260-265. doi: 10.18653/v1/W18-6238
Team
External Collaborators: | Shehan Perera | Madhavi Perera |
