Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
MARSYAS: a framework for audio analysis
Organised Sound
MICE: Aggregating and Classifying Meta Search Results into Self-Customized Categories
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Mapping search results into self-customized category hierarchy
Intelligent information processing II
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Categories are used to organize information and knowledge in directory system, folder etc. As the amount of information increase and the types of information diversify, it is common to have more categories created. As the number of categories increases, it becomes more difficult to organize, manage and look up information from existing categories. In this paper, categories are annotated with concept features to facilitate the access, retrieval and sharing of information in the categories. We have observed that training texts is crucial in learning the concept of a category and serves as a good measure to help human to construct the category model. Hence, we present a study on training texts selection and evaluate the effectiveness of training texts, as well as its capability to complement human's knowledge in constructing the category model. Experimental evaluation shows that using training texts approach in category model construction gives promising results in both effectiveness and complement measures.