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Exploiting the Wikipedia structure in local and global classification of taxonomic relations
Abstract Determining whether two terms have an anceDetermining 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.the existing well-known knowledge sources.
Abstractsub Determining whether two terms have an anceDetermining 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.the existing well-known knowledge sources.
Bibtextype article  +
Doi 10.1017/S1351324912000046  +
Has author Do Q.X. + , Dan Roth +
Has extra keyword Algorithmic approach + , Background knowledge + , Constraint-based + , Essential component + , Experimental evaluation + , General purpose + , Hierarchical structures + , Inference process + , Knowledge base + , Knowledge sources + , Learning-based approach + , Natural language processing applications + , Question answering + , Relational constraint + , Textual entailment + , Wikipedia + , Automotive industry + , Computational linguistics + , Knowledge based systems + , Learning algorithms + , Natural language processing systems +
Issn 13513249  +
Issue 2  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 235–262  +
Published in Natural Language Engineering +
Title Exploiting the Wikipedia structure in local and global classification of taxonomic relations +
Type journal article  +
Volume 18  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 17:21:25  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 17:21:25  +
DateThis property is a special property in this wiki. 2012  +
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