Titlebook: Bayesian Analysis in Natural Language Processing, Second Edition; Shay Cohen Book 2019Latest edition Springer Nature Switzerland AG 2019

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Introduction, a computer. As such, it borrows ideas from Artificial Intelligence, Linguistics, Machine Learning, Formal Language Theory and Statistics. In NLP, natural language is usually represented as written text (as opposed to speech signals, which are more common in the area of Speech Processing).

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Priors,uce the machinery used in Bayesian NLP. At their core, priors are distributions over a set of hypotheses, or when dealing with parametric model families, over a set of parameters. In essence, the prior distribution represents the prior beliefs that the modeler has about the identity of the parameter

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Sampling Methods,ead of approximate inference relies on the ability to simulate from the posterior in order to draw structures or parameters from the underlying distribution represented by the posterior. The samples drawn from this posterior can be averaged to approximate expectations (or normalization constants). I

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Nonparametric Priors,uster index (corresponding to a mixture component) followed by a draw from a cluster-specific distribution over words. Each distribution associated with a given cluster can be defined so that it captures specific distributional properties of the words in the vocabulary, or identifies a specific cate

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Representation Learning and Neural Networks,rning (such as with linear models). Continuous data representations of the data are directly extracted from simple, raw forms of data (such as indicator vectors which represent word co-occurrence in a sentence) and these data representations act as a substitute for feature templates used as part of linear models.

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