A comparative study of an unsupervised word sense disambiguation approach

Wei Xiong, Min Song, Lori Watrous deDeversterre

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 publicationApplied Natural Language Processing
Subtitle of host publicationIdentification, Investigation and Resolution
PublisherIGI Global
Pages412-422
Number of pages11
ISBN (Print)9781609607418
DOIs
Publication statusPublished - 2011 Dec 1

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Xiong, W., Song, M., & deDeversterre, L. W. (2011). A comparative study of an unsupervised word sense disambiguation approach. In Applied Natural Language Processing: Identification, Investigation and Resolution (pp. 412-422). IGI Global. https://doi.org/10.4018/978-1-60960-741-8.ch024