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: Nisansa de Silva
The Knowledge Representation project investigates methods for structuring, aligning, and reasoning over knowledge extracted from language, with particular emphasis on low-resource settings, domain-specific semantics, and intelligent information access.
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.
Dissertations
Conference Papers
Nisansa de Silva and Dejing Dou, "Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks", in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021, pp. 405--415. doi: 10.18653/v1/2021.eacl-main.31
Nisansa de Silva and Dejing Dou, "Semantic Oppositeness Embedding Using an Autoencoder-based Learning Model", in Database and Expert Systems Applications, 2019, pp. 159--174. doi: 10.1007/978-3-030-27615-7_12
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
