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 (US)
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
StatePublished - Mar 31 2013

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Medicine(all)

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