Distributional models of semantics capture word meaning very effectively, and they have been recently extended to account for compositionally-obtained representations of phrases made of content words. We explore whether compositional distributional semantic models can also handle a construction in which grammatical terms play a crucial role, namely determiner phrases (DPs). We introduce a new publicly available dataset to test distributional representations of DPs, and we evaluate state-of-The-art models on this set.

A relatedness benchmark to test the role of determiners in compositional distributional semantics

Bernardi, Raffaella;Dinu, Georgiana;Marelli, Marco;Baroni, Marco
2013-01-01

Abstract

Distributional models of semantics capture word meaning very effectively, and they have been recently extended to account for compositionally-obtained representations of phrases made of content words. We explore whether compositional distributional semantic models can also handle a construction in which grammatical terms play a crucial role, namely determiner phrases (DPs). We introduce a new publicly available dataset to test distributional representations of DPs, and we evaluate state-of-The-art models on this set.
2013
Proceedings of ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics) Volume 2: Short Papers
East Stroudsburg PA
Association for Computational Linguistics (ACL)
9781937284510
Bernardi, Raffaella; Dinu, Georgiana; Marelli, Marco; Baroni, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/33119
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