Keywords Extracting as Text Chance Discovery

  • Authors:

  • Zhenya Zhang;Honemei Cheng

  • Affiliations:

  • University of Science and Technology of China, Hefei;Management Engineering Department of AIA, Hefei, China

  • Venue:
  • FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
  • Year:
  • 2007

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Abstract

Keyword auto-extracting is focused by researchers on information retrieval, data mining, chance discovery and others application. In this paper, new algorithm, CCG(Cognition & Concept Graph, for text chance discovery is presented based on cognition with data depth as measurement. When the keywords in a document are treated as chances in the document, those keywords can be extracted by CGC automatically. In CGC, concepts of a document are represented as maximum connected sub graphs of the basic graph for the document and the cognition of reader/author on a term is weighted with data depth. The correlation for word and concept is defined and the formula for the correlation calculating is given. Experimental results show that keywords extracted by CCG can describe the document and author/reader's cognition much better than keywords extracted by others technologies such as frequency accumulating or KeyGraph.