A new topic filter based on maximum entropy model

Authors:
Chen Chen;Huilin Liu;Guoren Wang;Lili Yu
Affiliations:
Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Liaoning, Shenyang, China and College of Information Science and Engineering, Northeastern University, Li ...;Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Liaoning, Shenyang, China and College of Information Science and Engineering, Northeastern University, Li ...;Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Liaoning, Shenyang, China and College of Information Science and Engineering, Northeastern University, Li ...;College of Information Science and Engineering, Northeastern University, Liaoning, Shenyang, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Year:
2009

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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.