Distributional clustering of words for text classification
This paper describes the application of Distributional Clustering [20] to document classification. This approach clusters words into groups based on the distribution of class labels associated with each word. Thus, unlike some other unsupervised dimensionalityreduction techniques, such as Latent Semantic Indexing, we are able to compress the feature space much more aggressively, while still maintaining high document classification accuracy. Experimental results obtained on three real-world data sets show that we can reduce the feature dimensional&y by three orders of magnitude and lose only 2% accuracy-significantly better than Latent Semantic Indexing [6], class-based clustering [l], feature selection by mutual information [23], or Markov-blanket-based feature selection [13]. We also show that less aggressive clustering sometimes results in improved classification accuracy over classification without clustering.