Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
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A comparison of search term weighting: term relevance vs. inverse document frequency
SIGIR '81 Proceedings of the 4th annual international ACM SIGIR conference on Information storage and retrieval: theoretical issues in information retrieval
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Supervised term weighting for automated text categorization
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ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In text categorization, term weighting methods assign appropriate weights to the terms to improve the classification performance. In this study, we propose an effective term weighting scheme, i.e. tf.rf, and investigate several widely-used unsupervised and supervised term weighting methods on two popular data collections in combination with SVM and kNN algorithms. From our controlled experimental results, not all supervised term weighting methods have a consistent superiority over unsupervised term weighting methods. Specifically, the three supervised methods based on the information theory, i.e. tf.χ2, tf.ig and tf.or, perform rather poorly in all experiments. On the other hand, our proposed tf.rf achieves the best performance consistently and outperforms other methods substantially and significantly. The popularly-used tf.idf method has not shown a uniformly good performance with respect to different data corpora.