An unsupervised vector approach to biomedical term disambiguation: integrating UMLS and Medline

Authors:
Bridget T. McInnes
Affiliations:
University of Minnesota Twin Cities, Minneapolis, MN
Venue:
HLT-SRWS '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop
Year:
2008

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Abstract

This paper introduces an unsupervised vector approach to disambiguate words in biomedical text that can be applied to all-word disambiguation. We explore using contextual information from the Unified Medical Language System (UMLS) to describe the possible senses of a word. We experiment with automatically creating individualized stoplists to help reduce the noise in our dataset. We compare our results to SenseClusters and Humphrey et al. (2006) using the NLM-WSD dataset and with SenseClusters using conflated data from the 2005 Medline Baseline.