A comparative study of an unsupervised word sense disambiguation approach

Wei Xiong; Min Song; Lori Watrous DeVersterre

doi:10.4018/978-1-4666-3604-0.ch066

A comparative study of an unsupervised word sense disambiguation approach

Wei Xiong, Min Song, Lori Watrous DeVersterre

Department of Library and Information Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Bioinformatics
Subtitle of host publication	Concepts, Methodologies, Tools, and Applications
Publisher	IGI Global
Pages	1306-1316
Number of pages	11
Volume	3
ISBN (Electronic)	9781466636057
ISBN (Print)	1466636041, 9781466636040
DOIs	https://doi.org/10.4018/978-1-4666-3604-0.ch066
Publication status	Published - 2013 Mar 31

Bibliographical note

Funding Information:
Financial support from Academy of Finland is gratefully acknowledged (Grant Number 111692). The author would also like to thank Johnny Lindroos, Fredrick Sundell and Marketta Hiisa for their contribution to the project and their assistance in carrying out some of the experiments.

Publisher Copyright:
© 2013, IGI Global.

All Science Journal Classification (ASJC) codes

Computer Science(all)
Medicine(all)

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10.4018/978-1-4666-3604-0.ch066

Cite this

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AB - 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.

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A comparative study of an unsupervised word sense disambiguation approach

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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