Distributional Clustering of Words for Text Classification

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Distributional Clustering of Words for Text Classification (1998)

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by L. Douglas Baker , Andrew Kachites Mccallum

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@MISC{Baker98distributionalclustering,
    author = {L. Douglas Baker and Andrew Kachites Mccallum},
    title = {Distributional Clustering of Words for Text Classification},
    year = {1998}
}

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Abstract

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 dimensionality by three orders of magnitude and lose only 2% accuracy---significantly better than Latent Semantic Indexing [6], class-based clustering [1], 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. 1 Introduction The popularity of the Internet has caused an exponent...

Keyphrases

distributional clustering text classification latent semantic indexing feature space unsupervised dimensionalityreduction technique mutual information experimental result real-world data set class label improved classification accuracy feature dimensionality class-based clustering aggressive clustering high document classification accuracy feature selection markov-blanket-based feature selection

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