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Abstract: We present a general framework for distributional similarity based on the concepts of precision and recall. Different parameter settings within this framework approximate di erent existing similarity measures as well as many more which have, until now, been unexplored. We show that optimal parameter settings outperform two existing state-of-the-art similarity measures on two evaluation tasks for high and low frequency nouns. (Update)
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BibTeX entry: (Update)
Julie Weeds and David Weir, A general framework for distributional similarity, Proceedings of the Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. http://citeseer.ist.psu.edu/693665.html More
@misc{ weeds-general,
author = "J. Weeds and D. Weir",
title = "A general framework for distributional similarity",
text = "Julie Weeds and David Weir, A general framework for distributional similarity,
Proceedings of the Conference on Empirical Methods in Natural Language Processing
and Very Large Corpora.",
url = "citeseer.ist.psu.edu/693665.html" }
Citations not processed or no citations identified.
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