A Compositional and Interpretable Semantic Space
| Data(s) |
01/06/2015
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| Resumo |
Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion of composition. Our method learns a VSM that is both tailored to support a chosen semantic composition operation, and whose resulting features have an intuitive interpretation. Interpretability allows for the exploration of phrasal semantics, which we leverage to analyze performance on a behavioral task. |
| Identificador | |
| Idioma(s) |
eng |
| Publicador |
The Association for Computational Linguistics |
| Direitos |
info:eu-repo/semantics/closedAccess |
| Fonte |
Fyshe , A , Wehbe , L , Talukdar , P , Murphy , B & Mitchell , T 2015 , A Compositional and Interpretable Semantic Space . in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . The Association for Computational Linguistics , pp. 32-41 , 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) , Denver , United States , 31-5 June . |
| Tipo |
contributionToPeriodical |
