Part-of-speech enhanced context recognition

Abstract

Language independent 'bag-of-words' representations are surprisingly effective for text classification. In this communication our aim is to elucidate the synergy between language independent features and simple language model features. We consider term tag features estimated by a so-called part-of-speech tagger. The feature sets are combined in an early binding design with an optimized binding coefficient that allows weighting of the relative variance contributions of the participating feature sets. With the combined features documents classified using a latent semantic indexing representation and a probabilistic neural network classifier. Three medium size data-sets are analyzed and we find consistent synergy between the term and natural language features in all three sets for a range of training set sizes. The most significant enhancement is found for small text databases where high recognition rates are possible