Distributional Clustering Of English Words

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Distributional Clustering Of English Words (1993)

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by Fernando Pereira , Naftali Tishby , Lillian Lee

Venue:	In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics
Citations:	624 - 28 self

BibTeX

@INPROCEEDINGS{Pereira93distributionalclustering,
    author = {Fernando Pereira and Naftali Tishby and Lillian Lee},
    title = {Distributional Clustering Of English Words},
    booktitle = {In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics},
    year = {1993},
    pages = {183--190}
}

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Abstract

We describe and evaluate experimentally a method for clustering words according to their dis- tribution in particular syntactic contexts. Words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used as the similarity measure for clustering. Clusters are represented by average context distributions derived from the given words according to their probabilities of cluster membership. In many cases, the clusters can be thought of as encoding coarse sense distinctions. Deterministic annealing is used to find lowest distortion sets of clusters: as the an- nealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchi- cal "soft" clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.

Keyphrases

distributional clustering english word many case lowest distortion set similarity measure word coocurrence class model particular syntactic context relative entropy cluster membership coarse sense distinction hierarchi cal soft clustering dis tribution held-out test data deterministic annealing relative frequency distribution nealing parameter increase average context distribution

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