A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

Wei Xiong (New Jersey Institute of Technology, USA), Min Song (New Jersey Institute of Technology, USA) and Lori deVersterre (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-60960-741-8.ch024


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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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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