Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 25, Issue 6, Pages 1307-1322Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2012.30
Keywords
Semantic similarity; pointwise mutual information; automatic thesaurus generation; corpus statistics
Categories
Funding
- AFOSR [FA9550-08-1-0265]
- Microsoft
- Human Language Technology Center of Excellence
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1228673] Funding Source: National Science Foundation
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Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, PMImax, that augments PMI with information about a word's number of senses. The coefficients of PMImax are determined empirically by maximizing a utility function based on the performance of automatic thesaurus generation. We show that it outperforms traditional PMI in the application of automatic thesaurus generation and in two word similarity benchmark tasks: human similarity ratings and TOEFL synonym questions. PMImax achieves a correlation coefficient comparable to the best knowledge-based approaches on the Miller-Charles similarity rating data set.
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