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Computational Linguistics

Paola Merlo, Editor
September 2014, Vol. 40, No. 3, Pages 587-631
(doi: 10.1162/COLI_a_00194)
@ 2014 Association for Computational Linguistics
Probabilistic Distributional Semantics with Latent Variable Models
Article PDF (396.79 KB)

We describe a probabilistic framework for acquiring selectional preferences of linguistic predicates and for using the acquired representations to model the effects of context on word meaning. Our framework uses Bayesian latent-variable models inspired by, and extending, the well-known Latent Dirichlet Allocation (LDA) model of topical structure in documents; when applied to predicate–argument data, topic models automatically induce semantic classes of arguments and assign each predicate a distribution over those classes. We consider LDA and a number of extensions to the model and evaluate them on a variety of semantic prediction tasks, demonstrating that our approach attains state-of-the-art performance. More generally, we argue that probabilistic methods provide an effective and flexible methodology for distributional semantics.