Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning - Special issue on learning with probabilistic representations
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Yahoo! as an ontology: using Yahoo! categories to describe documents
Proceedings of the eighth international conference on Information and knowledge management
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Adaptive educational hypermedia on the web
Communications of the ACM - The Adaptive Web
Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A New Probabilistic Model of Text Classification and Retrieval TITLE2:
A New Probabilistic Model of Text Classification and Retrieval TITLE2:
Document classification using a finite mixture model
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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The huge amount of information available on the Web can help in building domain knowledge of a Web-based tutoring system. Therefore, we are in need of a way to classify this information at a suitable place. To overcome this challenge, we develop a dominant meanings classification model. This model constructs domain knowledge as a hierarchy of concepts. Each concept consists of some dominant meanings, and each of those is linked with some chunks (information fragments) to define it. The dominant meanings are a set of keywords that best fit an indented meaning of a target word (concept). The more dominant meanings, the better a concept relates to its chunk context. We investigated the effect of using this model to extract features on classifying Web information. We compared the model's results with Naïve Bayes classifiers. Our experiment showed that using this approach greatly improves the classification task.