Conversational Topic Classification using SVMs and Features Induced by Clustering
نویسنده
چکیده
This work explores the use of Support Vector Machines (SVM) for topic classification of conversations. An All-vs-One SVM system is used as the baseline. Several methods in feature weight scaling and feature selection are compared. Results suggest that the conversation domain requires a different set of methods from the written text domain. Finally, a feature selection method based on hierarchical clustering is presented with promising results.
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تاریخ انتشار 2004