Random-walk term weighting for improved text classification

Abstract

This paper describes a new approach for estimating term weights in a text classification task. The approach uses term co-occurrence as a measure of dependency between word features. A random walk model is applied on a graph encoding words and co-occurrence dependencies, resulting in scores that represent a quantification of how a particular word feature contributes to a given context. We argue that by modeling feature weights using these scores, as opposed to the traditional frequency-based scores, we can achieve better results in a text classification task. Experiments performed on four standard classification datasets show that the new random-walk based approach outperforms the traditional term frequency approach to feature weighting.