A quantitative assessment of SENSATIONAL with an exploration of its applications

Document Type

Conference Proceeding

Publication Date

10-19-2010

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 clustering technique. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Identifier

77957874657 (Scopus)

ISBN

[9781577354475]

Publication Title

Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference Flairs 23

First Page

289

Last Page

294

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