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Distributional clustering of English words
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Source Annual Meeting of the ACL archive
Proceedings of the 31st annual meeting on Association for Computational Linguistics table of contents
Columbus, Ohio
Pages: 183 - 190  
Year of Publication: 1993
Authors
Fernando Pereira  AT&T Bell Laboratories, Murray Hill, NJ
Naftali Tishby  Hebrew University, Jerusalem, Israel
Lillian Lee  Cornell University, Ithaca, NY
Publisher
Association for Computational Linguistics  Morristown, NJ, USA
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Downloads (6 Weeks): 15,   Downloads (12 Months): 96,   Citation Count: 155
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DOI Bookmark: 10.3115/981574.981598

ABSTRACT

We describe and evaluate experimentally a method for clustering words according to their distribution 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 annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical "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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Fernando Pereira: colleagues
Naftali Tishby: colleagues
Lillian Lee: colleagues

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