Difference between revisions of "Exploiting the Wikipedia Structure in Local and Global Classification of Taxonomic Relations"
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| − | '''Exploiting the Wikipedia Structure in Local and Global Classification of Taxonomic Relations''' - scientific work related to Wikipedia quality published in 2012, written by Quang Xuan Do and Dan Roth. | + | '''Exploiting the Wikipedia Structure in Local and Global Classification of Taxonomic Relations''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Quang Xuan Do]] and [[Dan Roth]]. |
== Overview == | == Overview == | ||
| − | 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, authors 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, authors 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. Authors experimental evaluation shows that approach significantly outperforms other systems built upon the existing well-known knowledge sources. | + | 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, authors 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, authors 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. Authors experimental evaluation shows that approach significantly outperforms other systems built upon the existing well-known knowledge sources. |
Revision as of 06:00, 10 June 2019
Exploiting the Wikipedia Structure in Local and Global Classification of Taxonomic Relations - scientific work related to Wikipedia quality published in 2012, written by Quang Xuan Do and Dan Roth.
Overview
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, authors 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, authors 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. Authors experimental evaluation shows that approach significantly outperforms other systems built upon the existing well-known knowledge sources.
