Word Sense Disambiguation is the task of automatically identifying the correct sense of a term with multiple senses. The goal of this research is to explore word sense disambiguation methods utilizing information from the Unified Medical Language System (UMLS).

Projects

Publications

• Exploiting MeSH Indexing in MEDLINE to Generate a Data set For Word Sense Disambiguation. Antonio Jimen-Yepes, Bridget T. McInnes and Alan R. Aronson. BMC Bioinformatics. 2011 Jun 2;12(1):223. [Epub ahead of print].

• Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation. Bridget T. McInnes, Ted Pedersen, Ying Liu, Serguei Pakhomov, and Genevieve B. Melton. Appears in the Proceedings of the Fifteenth Conference on Computational Natural Language Learning (CoNLL 2011), June 23-24, 2011, pp. 145 - 153, Portland, Oregon.

• Collocation Analysis for UMLS Knowledge-based Word Sense Disambiguation Antonio Jimen-Yepes, Bridget T. McInnes and Alan R. Aronson. BMC Bioinformatics. 2011, 12(Suppl 3):S4.

• Supervised and Knowledge-based Methods for Disambiguating Terms in Biomedical Text using the UMLS and MetaMap. Bridget T. McInnes. Doctor of Philosophy Dissertation, Department of Computer Science, University of Minnesota, Twin Cities, September, 2009. (slides: pptx )

• An Unsupervised Vector Approach to Biomedical Term Disambiguation: Integrating UMLS and Medline. Bridget T. McInnes. In Proceedings of the Assocation for Computational Linguistics Student Research Workshop (ACL-SRW) 2008. (poster: pdf)

• Using UMLS Concept Unique Identifiers (CUIs) for Word Sense Disambiguation in the Biomedical Domain. Bridget T. McInnes, Ted Pedersen, and John Carlis. In Proceedings of the Annual Symposium of the American Medical Informatics Association (AMIA), pages 533-37, Nov. 2007, Chicago, IL. (slides: pdf ppt )

Datasets

• NLM-WSD dataset
• MSH-WSD dataset
• Abbrev dataset