A Markovian approach to distributional semantics with application to semantic compositionality

Edouard Grave 1, 2, 3 Guillaume Obozinski 4, 5 Francis Bach 2, 3
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
5 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : In this article, we describe a new approach to distributional semantics. This approach relies on a generative model of sentences with latent variables, which takes the syntax into account by using syntactic dependency trees. Words are then represented as posterior distributions over those latent classes, and the model allows to naturally obtain in-context and out-of-context word representations, which are comparable. We train our model on a large corpus and demonstrate the compositionality capabilities of our approach on different datasets.
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https://hal.inria.fr/hal-01080309
Contributor : Edouard Grave <>
Submitted on : Monday, December 15, 2014 - 8:06:10 PM
Last modification on : Friday, June 28, 2019 - 3:01:15 PM
Long-term archiving on : Monday, March 16, 2015 - 12:46:08 PM

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Edouard Grave, Guillaume Obozinski, Francis Bach. A Markovian approach to distributional semantics with application to semantic compositionality. International Conference on Computational Linguistics (Coling), International Committee on Computational Linguistics (ICCL), Aug 2014, Dublin, Ireland. pp.1447 - 1456. ⟨hal-01080309⟩

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