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The hidden Markov Topic model: a probabilistic model of semantic representation
Mark Andrews1, Gabriella Vigliocco
1Department of Cognitive, Perceptual, and Brain Sciences, Division of Psychology and Language Sciences, University College London.
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.
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