Minimally Supervised Text Classification Using von Neumann Kernel

摘要

Kernel methods such as support vector machine (SVM) have become highly popular in the task of text classification. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data whichis the prohibitive characteristics of textual data representation. A significant challenge in text classification is to reduce the need for labeled training data while maintaining an acceptable performance. In this paper, we present a semi-supervised technique using the von Neumann kernelfor text classification. Specifically, the semantic similarities between terms are first determined with both labeled and unlabeled training data by means of a diffusion process on a graph defined by lexicon and co-occurrence information, and the von Neumann kernel is then constructed basedon the learned semantic similarity. Finally, the SVM classifier trains a model for each class during the training phase and this model is then applied to all test examples in the test phase. The main feature of this approach is that it takes advantage of the von Neumann kernel to reveal thesemantic similarities between terms in an unsupervised manner, which provides a kernel framework for semi-supervised learning. Experiments on several benchmark data sets demonstrate the proposed approach is sound and effective.