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Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach

Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach

Torsten Schiemann, Ulf Leser, Jörg Hakenberg
Copyright: © 2009 |Pages: 20
ISBN13: 9781605662749|ISBN10: 1605662747|ISBN13 Softcover: 9781616925284|EISBN13: 9781605662756
DOI: 10.4018/978-1-60566-274-9.ch008
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MLA

Schiemann, Torsten, et al. "Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach." Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, IGI Global, 2009, pp. 142-161. https://doi.org/10.4018/978-1-60566-274-9.ch008

APA

Schiemann, T., Leser, U., & Hakenberg, J. (2009). Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach. In V. Prince & M. Roche (Eds.), Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (pp. 142-161). IGI Global. https://doi.org/10.4018/978-1-60566-274-9.ch008

Chicago

Schiemann, Torsten, Ulf Leser, and Jörg Hakenberg. "Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach." In Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, 142-161. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9.ch008

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Abstract

Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein encoded by the gene, and a disease associated with the protein share the same name. Resolving this problem, that is, assigning to an ambiguous word in a given context its correct meaning is called word sense disambiguation (WSD). It is a pre-requisite for associating entities in text to external identifiers and thus to put the results from text mining into a larger knowledge framework. In this chapter, we introduce the WSD problem and sketch general approaches for solving it. The authors then describe in detail the results of a study in WSD using classification. For each sense of an ambiguous term, they collected a large number of exemplary texts automatically and used them to train an SVM-based classifier. This method reaches a median success rate of 97%. The authors also provide an analysis of potential sources and methods to obtain training examples, which proved to be the most difficult part of this study.

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