A re-examination of text categorization methods
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A concept-based model for enhancing text categorization
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WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Semantic patterns for user-interactive question answering
Concurrency and Computation: Practice & Experience - Second International Conference on Semantics, Knowledge and Grid (SKG2006)
A Web-Based Platform for User-Interactive Question-Answering
World Wide Web
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Question categorization, which suggests one of a set of predefined categories to a user's question according to the question's topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions.