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Bayesian Analysis in Natural Language Processing, Second Edition (Paperback, 2nd Revised edition)
Loot Price: R1,867
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Bayesian Analysis in Natural Language Processing, Second Edition (Paperback, 2nd Revised edition)
Series: Synthesis Lectures on Human Language Technologies
Expected to ship within 10 - 15 working days
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Natural language processing (NLP) went through a profound
transformation in the mid-1980s when it shifted to make heavy use
of corpora and data-driven techniques to analyze language. Since
then, the use of statistical techniques in NLP has evolved in
several ways. One such example of evolution took place in the late
1990s or early 2000s, when full-fledged Bayesian machinery was
introduced to NLP. This Bayesian approach to NLP has come to
accommodate various shortcomings in the frequentist approach and to
enrich it, especially in the unsupervised setting, where
statistical learning is done without target prediction examples. In
this book, we cover the methods and algorithms that are needed to
fluently read Bayesian learning papers in NLP and to do research in
the area. These methods and algorithms are partially borrowed from
both machine learning and statistics and are partially developed
"in-house" in NLP. We cover inference techniques such as Markov
chain Monte Carlo sampling and variational inference, Bayesian
estimation, and nonparametric modeling. In response to rapid
changes in the field, this second edition of the book includes a
new chapter on representation learning and neural networks in the
Bayesian context. We also cover fundamental concepts in Bayesian
statistics such as prior distributions, conjugacy, and generative
modeling. Finally, we review some of the fundamental modeling
techniques in NLP, such as grammar modeling, neural networks and
representation learning, and their use with Bayesian analysis.
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