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A graph-based approach to named entity categorization in Wikipedia using conditional random fields
Abstract This paper presents a method for categorizThis paper presents a method for categorizing named entities in Wikipedia. In Wikipedia, an anchor text is glossed in a linked HTML text. We formalize named entity categorization as a task of categorizing anchor texts with linked HTML texts which glosses a named entity. Using this representation, we introduce a graph structure in which anchor texts are regarded as nodes. In order to incorporate HTML structure on the graph, three types of cliques are defined based on the HTML tree structure. We propose a method with Conditional Random Fields (CRFs) to categorize the nodes on the graph. Since the defined graph may include cycles, the exact inference of CRFs is computationally expensive. We introduce an approximate inference method using Treebased Reparameterization (TRP) to reduce computational cost. In experiments, our proposed model obtained significant improvements compare to baseline models that use Support Vector Machines.e models that use Support Vector Machines.
Abstractsub This paper presents a method for categorizThis paper presents a method for categorizing named entities in Wikipedia. In Wikipedia, an anchor text is glossed in a linked HTML text. We formalize named entity categorization as a task of categorizing anchor texts with linked HTML texts which glosses a named entity. Using this representation, we introduce a graph structure in which anchor texts are regarded as nodes. In order to incorporate HTML structure on the graph, three types of cliques are defined based on the HTML tree structure. We propose a method with Conditional Random Fields (CRFs) to categorize the nodes on the graph. Since the defined graph may include cycles, the exact inference of CRFs is computationally expensive. We introduce an approximate inference method using Treebased Reparameterization (TRP) to reduce computational cost. In experiments, our proposed model obtained significant improvements compare to baseline models that use Support Vector Machines.e models that use Support Vector Machines.
Bibtextype inproceedings  +
Has author Watanabe Y. + , Asahara M. + , Matsumoto Y. +
Has extra keyword Approximate inference + , Baseline models + , Computational costs + , Conditional random field + , Exact inference + , Graph structures + , Graph-based + , Named entities + , Reparameterization + , Tree structures + , Tree-based + , Wikipedia + , Conditional random fields (CRFs) + , Computational linguistics + , Content based retrieval + , HTML + , Trees (mathematics) + , Forestry + , Graphic methods + , Random processes + , Websites + , Natural language processing systems +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 649–657  +
Published in EMNLP-CoNLL 2007 - Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning +
Title A graph-based approach to named entity categorization in Wikipedia using conditional random fields +
Type conference paper  +
Year 2007 +
Creation dateThis property is a special property in this wiki. 6 November 2014 12:19:33  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without DOI parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 12:19:33  +
DateThis property is a special property in this wiki. 2007  +
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A graph-based approach to named entity categorization in Wikipedia using conditional random fields + Title
 

 

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