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A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network
Abstract It is a fundamental and important task to It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated. Based on a commonly accepted assumption that the title of a document is always elaborated to reflect the content of a document and consequently key phrases tend to have close semantics to the title, we propose a novel semi-supervised key phrase extraction approach in this paper by computing the phrase importance in the semantic network, through which the influence of title phrases is propagated to the other phrases iteratively. Experimental results demonstrate the remarkable performance of this approach.e remarkable performance of this approach.
Abstractsub It is a fundamental and important task to It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated. Based on a commonly accepted assumption that the title of a document is always elaborated to reflect the content of a document and consequently key phrases tend to have close semantics to the title, we propose a novel semi-supervised key phrase extraction approach in this paper by computing the phrase importance in the semantic network, through which the influence of title phrases is propagated to the other phrases iteratively. Experimental results demonstrate the remarkable performance of this approach.e remarkable performance of this approach.
Bibtextype inproceedings  +
Has author Deyi Li + , Li S. + , Li W. + , Weiping Wang + , Qu W. +
Has extra keyword Binary relationships + , Document semantics + , Key-phrase + , Semantic network + , Semi-supervised + , Wikipedia + , Semantics + , Computational linguistics +
Isbn 9781617388088  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 296–300  +
Published in ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference +
Title A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 7 November 2014 00:44:53  +
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. 7 November 2014 00:44:53  +
DateThis property is a special property in this wiki. 2010  +
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A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network + Title
 

 

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