Paper: A Markovian approach to distributional semantics with application to semantic compositionality

ACL ID C14-1137
Title A Markovian approach to distributional semantics with application to semantic compositionality
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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.