A comparative study of an unsupervised word sense disambiguation approach

Wei Xiong, Min Song, Lori Watrous DeVersterre

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL's clustering technique.

Original languageEnglish
Title of host publicationBioinformatics
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages1306-1316
Number of pages11
Volume3
ISBN (Electronic)9781466636057
ISBN (Print)1466636041, 9781466636040
DOIs
Publication statusPublished - 2013 Mar 31

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Medicine(all)

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  • Cite this

    Xiong, W., Song, M., & DeVersterre, L. W. (2013). A comparative study of an unsupervised word sense disambiguation approach. In Bioinformatics: Concepts, Methodologies, Tools, and Applications (Vol. 3, pp. 1306-1316). IGI Global. https://doi.org/10.4018/978-1-4666-3604-0.ch066