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020 _a9783031021701
_9978-3-031-02170-1
024 7 _a10.1007/978-3-031-02170-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aCohen, Shay.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980556
245 1 0 _aBayesian Analysis in Natural Language Processing, Second Edition
_h[electronic resource] /
_cby Shay Cohen.
250 _a2nd ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXXXI, 311 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aList of Figures -- List of Figures -- List of Figures -- Preface (First Edition) -- Acknowledgments (First Edition) -- Preface (Second Edition) -- Preliminaries -- Introduction -- Priors -- Bayesian Estimation -- Sampling Methods -- Variational Inference -- Nonparametric Priors -- Bayesian Grammar Models -- Representation Learning and Neural Networks -- Closing Remarks -- Bibliography -- Author's Biography -- Index.
520 _aNatural 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_980557
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001819
776 0 8 _iPrinted edition:
_z9783031010422
776 0 8 _iPrinted edition:
_z9783031032981
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980558
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02170-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84982
_d84982