TL;DR
This paper compares various distributional similarity measures to improve probability estimates for unseen cooccurrences, classifies these functions, and introduces a new superior similarity measure.
Contribution
It provides an empirical comparison, a classification framework, and a novel similarity function for distributional similarity measures.
Findings
The new similarity function outperforms existing measures.
Classification of similarity functions based on information incorporated.
Empirical analysis of a broad range of measures.
Abstract
We study distributional similarity measures for the purpose of improving probability estimation for unseen cooccurrences. Our contributions are three-fold: an empirical comparison of a broad range of measures; a classification of similarity functions based on the information that they incorporate; and the introduction of a novel function that is superior at evaluating potential proxy distributions.
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Taxonomy
TopicsBayesian Methods and Mixture Models