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SePaS: Word sense disambiguation by sequential patterns in sentences

Published online by Cambridge University Press:  06 September 2013

MASOUD NAROUEI ,
MANSOUR AHMADI  and
ASHKAN SAMI
MASOUD NAROUEI
Affiliation:
Young Researchers and Elite Club, Zahedan Branch, Islamic Azad University, Zahedan, Iran e-mail: naroee@cse.shirazu.ac.ir
MANSOUR AHMADI
Affiliation:
Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran e-mail: info@mahmadi.com
ASHKAN SAMI
Affiliation:
Department of Computer Science, Shiraz University, Shiraz, Iran e-mail: asami@ieee.org
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Abstract

An open problem in natural language processing is word sense disambiguation (WSD). A word may have several meanings, but WSD is the task of selecting the correct sense of a polysemous word based on its context. Proposed solutions are based on supervised and unsupervised learning methods. The majority of researchers in the area focused on choosing proper size of ‘n’ in n-gram that is used for WSD problem. In this research, the concept has been taken to a new level by using variable ‘n’ and variable size window. The concept is based on the iterative patterns extracted from the text. We show that this type of sequential pattern is more effective than many other solutions for WSD. Using regular data mining algorithms on the extracted features, we significantly outperformed most monolingual WSD solutions. The state-of-the-art results were obtained using external knowledge like various translations of the same sentence. Our method improved the accuracy of the multilingual system more than 4 percent, although we were using monolingual features.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 2 , March 2015 , pp. 251 - 269
Copyright
Copyright © Cambridge University Press 2013 

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