In document classification it is common to represent a document as the so called bag-of-words form, which is essentially a global term distribution indicating how often certain terms appear in a text. Ignoring the spatial statistics (i.e., where in a text they appear) can potentially lead to a suboptimal solution. The key motivation or assumption in this paper is that there may exist underlying segmentation of sentences in a document, and perhaps this partitioning might be intuitively appealing

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... (e.g., each group corresponds to a particular sentiment or gist of arguments). If the segmentation is known somehow, terms belonging to the same/different groups can potentially be treated in an equal/different manner for classification. Based on the idea, we build a novel document classification model comprised of two parts: a sentence tagger that predicts the group labels of sentences, and a classifier that forms the input features as a weighted term frequency vector that is aggregated from all sentences but weighed differently cluster-wise according to the prediction in the first model. We suggest an efficient learning strategy for this model. For several benchmark document classification problems, we demonstrate that the proposed approach yields significantly improved classification performance over several existing algorithms.