A comparative study of an unsupervised word sense disambiguation approach

Document Type

Syllabus

Publication Date

12-1-2011

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. © 2012, IGI Global.

Identifier

84899252465 (Scopus)

ISBN

[9781609607418]

Publication Title

Applied Natural Language Processing Identification Investigation and Resolution

External Full Text Location

https://doi.org/10.4018/978-1-60960-741-8.ch024

First Page

412

Last Page

422

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