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Exploiting the Wikipedia structure in local and global classification of taxonomic relations*

Published online by Cambridge University Press:  14 March 2012

QUANG XUAN DO  and
DAN ROTH
QUANG XUAN DO
Affiliation:
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA emails: [email protected],[email protected]
DAN ROTH
Affiliation:
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA emails: [email protected],[email protected]
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Abstract

Determining whether two terms have an ancestor relation (e.g. Toyota Camry and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in Natural Language Processing applications such as Question Answering, Summarization, and Textual Entailment. Significant work has been done on developing knowledge sources that could support these tasks, but these resources usually suffer from low coverage, noise, and are inflexible when dealing with ambiguous and general terms that may not appear in any stationary resource, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a hierarchical structure of concepts and relations, we describe an algorithmic approach that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint-based inference process that leverages an existing knowledge base to enforce relational constraints among terms and thus improves the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon the existing well-known knowledge sources.

Type
Articles
Information
Natural Language Engineering , Volume 18 , Special Issue 2: Statistical Learning of Natural Language Structured Input and Output , April 2012 , pp. 235 - 262
Copyright
Copyright © Cambridge University Press 2012

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