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Mapping Arabic WordNet synsets to Wikipedia articles using monolingual and bilingual features

Published online by Cambridge University Press:  21 October 2015

ABDULGABBAR SAIF ,
MOHD JUZAIDDIN AB AZIZ  and
NAZLIA OMAR
ABDULGABBAR SAIF
Affiliation:
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia e-mail: [email protected], [email protected], [email protected]
MOHD JUZAIDDIN AB AZIZ
Affiliation:
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia e-mail: [email protected], [email protected], [email protected]
NAZLIA OMAR
Affiliation:
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia e-mail: [email protected], [email protected], [email protected]
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Abstract

The alignment of WordNet and Wikipedia has received wide attention from researchers of computational linguistics, who are building a new lexical knowledge source or enriching the semantic information of WordNet entities. The main challenge of this alignment is how to handle the synonymy and ambiguity issues in the contents of two units from different sources. Therefore, this paper introduces mapping method that links an Arabic WordNet synset to its corresponding article in Wikipedia. This method uses monolingual and bilingual features to overcome the lack of semantic information in Arabic WordNet. For evaluating this method, an Arabic mapping data set, which contains 1,291 synset–article pairs, is compiled. The experimental analysis shows that the proposed method achieves promising results and outperforms the state-of-the-art methods that depend only on monolingual features. The mapped method has also been used to increase the coverage of Arabic WordNet by inserting new synsets from Wikipedia.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 1 , January 2017 , pp. 53 - 91
Copyright
Copyright © Cambridge University Press 2015 

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