Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining

  • Chong Huang
  • Yonghong Tian
  • Tiejun Huang
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.


Feature Selection Term Frequency Semantic Network Query Word Keyword Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chong Huang
    • 1
  • Yonghong Tian
    • 2
  • Tiejun Huang
    • 1
    • 2
  • Wen Gao
    • 1
    • 2
  1. 1.Graduate SchoolChinese Academy of SciencesBeijingChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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