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JOURNALS // Program Systems: Theory and Applications // Archive

Program Systems: Theory and Applications, 2018 Volume 9, Issue 4, Pages 443–460 (Mi ps319)

This article is cited in 4 papers

Artificial Intelligence, Intelligent Systems, Neural Networks

A dependency-based distributional semantic model for identifying taxonomic similarity

I. V. Trofimov, E. A. Suleymanova

Ailamazyan Program Systems Institute of Russian Academy of Sciences

Abstract: Are dependency-based distributional semantic models worth the computational cost and the linguistic resources they require? As our evaluation study suggests, the answer should be "yes" if the task in hand involves distinguishing between feature-based similarity and pure association. After extensive parameter tuning, window-based models still fall behind dependency-based ones when evaluated on our Russian-language similarity/association dataset. (In Russian).

Key words and phrases: distributional semantic model, dependency-based DSM, taxonomic similarity, feature-based similarity, word2vec, skipgram, RuSim1000.

UDC: 004.85
BBK: 32.813

MSC: 68T50, 68T05

Received: 12.11.2018
05.12.2018
Accepted: 30.12.2018

DOI: 10.25209/2079-3316-2018-9-4-443-460



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