Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Optimizing search by showing results in context
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Term Weighting Approaches in Automatic Text Retrieval
Term Weighting Approaches in Automatic Text Retrieval
A Lazy Approach for Category Model Construction Using Training Texts
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
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With the rapid growth of online information, a simple search query may return thousands or even millions of results. There is a need to help user to access and identify relevant information in a flexible way. This paper describes a methodology that automatically map web search results into user defined categories. This allows the user to focus on categories of their interest, thus helping them to find for relevant information in less time. Text classification algorithm is used to map search results into categories. This paper focuses on feature selection method and term weighting measure in order to train an optimum and simple category model from a relatively small number of training texts. Experimental evaluations on real world data collected from the web shows that our classification algorithm gives promising results and can potentially be used to classify search results returned by search engines.