1998 DistributionalClusteringofWords

• (Baker & McCallum, 1998) ⇒ L. Douglas Baker, and Andrew Kachites McCallum. (1998). “Distributional Clustering of Words for Text Classification.” In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ISBN:1-58113-015-5 doi:10.1145/290941.290970

Subject Headings: Supervised Document Classification, Supervised Dimensionality Reduction.

Cited By

• http://scholar.google.com/scholar?q=%221998%22+Distributional+Clustering+of+Words+for+Text+Classification
• http://dl.acm.org/citation.cfm?id=290941.290970&preflayout=flat#citedby

Quotes

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 dimensionality-reduction 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 DistributionalClusteringofWords	L. Douglas Baker Andrew McCallum			Distributional Clustering of Words for Text Classification				10.1145/290941.290970		1998