Graph-Based Keyphrase Extraction Using Wikipedia

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Description

Keyphrases describe a document in a coherent and simple way, giving the prospective reader a way to quickly determine whether the document satisfies their information needs. The pervasion of huge amount of information on Web, with only a small amount of documents have keyphrases extracted, there is a definite need to discover automatic keyphrase extraction systems. Typically, a document written by human develops around one or more general concepts or sub-concepts. These concepts or sub-concepts should be structured and semantically related with each other, so that they can form the meaningful representation of a document. Considering the fact, the phrases ... continued below

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Dandala, Bharath December 2010.

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  • Dandala, Bharath

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Description

Keyphrases describe a document in a coherent and simple way, giving the prospective reader a way to quickly determine whether the document satisfies their information needs. The pervasion of huge amount of information on Web, with only a small amount of documents have keyphrases extracted, there is a definite need to discover automatic keyphrase extraction systems. Typically, a document written by human develops around one or more general concepts or sub-concepts. These concepts or sub-concepts should be structured and semantically related with each other, so that they can form the meaningful representation of a document. Considering the fact, the phrases or concepts in a document are related to each other, a new approach for keyphrase extraction is introduced that exploits the semantic relations in the document. For measuring the semantic relations between concepts or sub-concepts in the document, I present a comprehensive study aimed at using collaboratively constructed semantic resources like Wikipedia and its link structure. In particular, I introduce a graph-based keyphrase extraction system that exploits the semantic relations in the document and features such as term frequency. I evaluated the proposed system using novel measures and the results obtained compare favorably with previously published results on established benchmarks.

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  • December 2010

Added to The UNT Digital Library

  • Jan. 9, 2012, 9:53 p.m.

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  • Jan. 21, 2014, 1:47 p.m.

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Dandala, Bharath. Graph-Based Keyphrase Extraction Using Wikipedia, thesis, December 2010; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc67939/: accessed June 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .