Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy

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dc.contributor.authorHan, Lushan
dc.contributor.authorFinin, Tim
dc.contributor.authorMcNamee, Paul
dc.contributor.authorJoshi, Anupam
dc.contributor.authorYesha, Yelena
dc.date.accessioned2018-11-05T14:32:59Z
dc.date.available2018-11-05T14:32:59Z
dc.date.issued2013-06-01
dc.description.abstractPointwise 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 dataset.en_US
dc.description.sponsorshipThis research was supported by MURI award FA9550-08-1- 0265 from the Air Force Ofce of Scientic Research, NSF award IIS-0326460, a gift from Microsoft, and the Human Language Technology Center of Excellence.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/6152109en_US
dc.format.extent26 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprints
dc.identifierdoi:10.13016/M21C1TK4M
dc.identifier.citationHan, Lushan, Tim Finin, Paul McNamee, Anupam Joshi, and Yelena Yesha. “Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy.” IEEE Transactions on Knowledge and Data Engineering 25, no. 6 (June 2013): 1307–22. https://doi.org/10.1109/TKDE.2012.30.en_US
dc.identifier.urihttps://doi.org/10.1109/TKDE.2012.30
dc.identifier.urihttp://hdl.handle.net/11603/11854
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rights© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectontologyen_US
dc.subjectpointwise mutual informationen_US
dc.subjectsemantic similarityen_US
dc.subjectsemanticsen_US
dc.subjectsynonymsen_US
dc.subjectword similarityen_US
dc.subjectnatural language processingen_US
dc.subjectcorpus statisticsen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleImproving Word Similarity by Augmenting PMI with Estimates of Word Polysemyen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-6593-1792
dcterms.creatorhttps://orcid.org/0000-0002-8641-3193

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