A new topic filter based on maximum entropy model

Chen Chen*, Huilin Liu, Guoren Wang, Lili Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Because of the large web scale and the information requirement for special field, focuse2825453011d search has attracted more and more people. For the complexity of natural language, there are ambiguous for a word itself, and which will take some trouble for topic filter. For the two main problems, false positive and false negative, this paper proposes two new methods separately. By machine learning, we construct a guide model with the maximum entropy principle, by which we can filter the noise pages out easily and by KNN method, the false negative problem will be solved easily. The experiment shows that our model or method really outperforms the base-line method.

Original languageEnglish
Title of host publication6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Pages495-499
Number of pages5
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 - Tianjin, China
Duration: 14 Aug 200916 Aug 2009

Publication series

Name6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Volume7

Conference

Conference6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Country/TerritoryChina
CityTianjin
Period14/08/0916/08/09

Keywords

  • Focused search
  • KNN
  • Maximum entropy
  • Noise pages
  • Topic filter

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