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

Informatics

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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 (US)
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
State	Published - Mar 31 2013

All Science Journal Classification (ASJC) codes

General Computer Science
General Medicine

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

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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.",

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N2 - 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.

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

All Science Journal Classification (ASJC) codes

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