A Boosting Algorithm for Classification of Semi-Structured Text

The focus of research in text classification has expanded from simple topic identification to more challenging tasks such as opinion/modality identification. Unfortunately, the latter goals exceed the ability of the traditional bag-of-word representation approach, and a richer, more structural representation is required. Accordingly, learning algorithms must be created that can handle the structures observed in texts. In this paper, we propose a Boosting algorithm that captures sub-structures embedded in texts. The proposal consists of i) decision stumps that use subtrees as features and ii) the Boosting algorithm which employs the subtree-based decision stumps as weak learners. We also discuss the relation between our algorithm and SVMs with tree kernel. Two experiments on opinion/modality classification confirm that subtree features are important.