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This paper addresses XML document classification by considering both structural and content-based features of the documents. This approach leads to better constructing a set of informative feature vectors that represents both structural and textual aspects of XML documents. For this purpose, we integrate soft clustering of words and feature reduction into the process. To extract structural information, we employ an existing frequent tree-mining algorithm combined with an information gain filter to retrieve the most informative substructures from XML documents. However, for extracting content information, we propose soft clustering of words using each cluster as a textual feature. We have conducted extensive experiments on a benchmark dataset, namely 20NewsGroups, and an XML documents dataset given in LOGML that describes the web-server logs of user sessions. With regards to the classifier built only using our textual features, the results show that it outperforms a naive support-vector-machine (SVM)-based classifier, as well as an information retrieval classifier (IRC). We further demonstrate the effectiveness of incorporating both structural and content information into the process of learning, by comparing our classifier model and several XML document classifiers. In particular, by applying SVM and decision tree algorithms using our feature vector representation of XML documents dataset, we have achieved 85.79% and 87.04% classification accuracy, respectively, which are higher than accuracy achieved by XRules, a well-known structural-based XML document classifier.