A Markovian approach to distributional semantics with application to semantic compositionality - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

A Markovian approach to distributional semantics with application to semantic compositionality

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.
Fichier principal
Vignette du fichier
grave2014markovian.pdf (240.09 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01080309 , version 1 (15-12-2014)

Licence

Copyright

Identifiers

  • HAL Id : hal-01080309 , version 1

Cite

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⟩
338 View
223 Download

Share

Gmail Facebook X LinkedIn More