Text categorization by boosting automatically extracted concepts

This paper investigates the use of concept-based document representations to supplement word- or phrase-based features, and proposes to use AdaBoost to optimally combine weak hypotheses based on both types of features.

Abstract

Term-based representations of documents have found wide-spread use in information retrieval. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage.In this paper we investigate the use of concept-based document representations to supplement word- or phrase-based features. The utilized concepts are automatically extracted from documents via probabilistic latent semantic analysis. We propose to use AdaBoost to optimally combine weak hypotheses based on both types of features. Experimental results on standard benchmarks confirm the validity of our approach, showing that AdaBoost achieves consistent improvements by including additional semantic features in the learned ensemble.