title: The hidden Markov Topic model: a probabilistic model of semantic representation.
abstract: In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag-of-words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag-of-words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), preserving its strengths while extending its scope to incorporate more fine-grained linguistic information.
authors.affiliation: Department of Cognitive, Perceptual, and Brain Sciences, Division of Psychology and Language Sciences, University College London.
authors.forename: Mark
authors.initials: M
authors.lastname: Andrews
authors.forename: Gabriella
authors.initials: G
authors.lastname: Vigliocco
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007802:Language; D007858:Learning; D008390:Markov Chains; D015233:Models, Statistical; D012660:Semantics
keywords: Bayesian models; Computational models; Probabilistic models; Semantic memory; Semantic representation