A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

Wei Xiong, Min Song, Lori deVersterre
Copyright: © 2012 |Pages: 11
DOI: 10.4018/978-1-60960-741-8.ch024
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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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Background

There are three types of WSD techniques (Ide and Veronis 1998): supervised learning, unsupervised learning and knowledge-based WSD. Supervised techniques need manually-labeled examples for each ambiguous term in the data set to predict the correct sense of the same word in a new context. This is referred to as training material which allows their corpus to build up a classification scheme based on the set of feature-encoded inputs and their appropriate sense label or category. The result of this training is a classifier that can be applied to future instances of the ambiguous word.

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