000 03504nam a22005295i 4500
001 978-3-031-02161-9
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008 221028s2016 sz | s |||| 0|eng d
020 _a9783031021619
_9978-3-031-02161-9
024 7 _a10.1007/978-3-031-02161-9
_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
_979304
245 1 0 _aBayesian Analysis in Natural Language Processing
_h[electronic resource] /
_cby Shay Cohen.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aIV, 274 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 _aPreface -- Acknowledgments -- Preliminaries -- Introduction -- Priors -- Bayesian Estimation -- Sampling Methods -- Variational Inference -- Nonparametric Priors -- Bayesian Grammar Models -- 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 for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. 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. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling 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)
_979305
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001758
776 0 8 _iPrinted edition:
_z9783031010330
776 0 8 _iPrinted edition:
_z9783031032899
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_979306
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02161-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c84755
_d84755