Research in text classification currently focuses on challenging tasks such as sentiment classification, modality identification, and so on. In these tasks, approaches that use a structural representation, like a tree, have shown better performance rather than a bag-of-words representation. In this paper, we propose a boosting algorithm for classifying a text that is a set of sentences represented by tree. The algorithm learns rules represented by subtrees with their frequency information. Existing boosting-based algorithms use subtrees as features without considering their frequency because the existing algorithms targeted a sentence rather than a text. In contrast, our algorithm learns how the occurrence frequency of each subtree is important for classification. Experiments on topic identification of Japanese news articles and English sentiment classification shows the effectiveness of subtree features with their frequency.
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