Sale!

Bayesian Analysis in Natural Language Processing 2nd Edition by Shay Cohen ISBN 168173527X 9781681735276

Original price was: $50.00.Current price is: $35.00.

Bayesian Analysis in Natural Language Processing 2nd Edition Shay Cohen Digital Instant Download

Author(s): Shay Cohen
ISBN(s): 9781681735276, 168173527X
Edition: 2
File Details: PDF, 5.09 MB
Year: 2019
Language: english
SKU: EB-10437834 Category: Tag:

Bayesian Analysis in Natural Language Processing 2nd Edition by Shay Cohen – Ebook PDF Instant Download/Delivery: 168173527X, 9781681735276
Full download Bayesian Analysis in Natural Language Processing 2nd Edition after payment

Product details:

ISBN 10: 168173527X 
ISBN 13: 9781681735276
Author: Shay Cohen

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 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.

Bayesian Analysis in Natural Language Processing 2nd Table of contents:

  1. Probability Measures
  2. Random Variables
  3. Continuous and Discrete Random Variables
  4. Joint Distribution over Multiple Random Variables
  5. Conditional Distributions
  6. Bayes’ Rule
  7. Independent and Conditionally Independent Random Variables
  8. Exchangeable Random Variables
  9. Expectations of Random Variables
  10. Models
  11. Parametric vs. Nonparametric Models
  12. Inference with Models
  13. Generative Models
  14. Independence Assumptions in Models
  15. Directed Graphical Models
  16. Learning from Data Scenarios
  17. Bayesian and Frequentist Philosophy (Tip of the Iceberg)
  18. Summary
  19. Exercises
  20. Introduction
  21. Overview: Where Bayesian Statistics and NLP Meet
  22. First Example: The Latent Dirichlet Allocation Model
  23. The Dirichlet Distribution
  24. Inference
  25. Summary
  26. Second Example: Bayesian Text Regression
  27. Conclusion and Summary
  28. Exercises
  29. Priors
  30. Conjugate Priors
  31. Conjugate Priors and Normalization Constants
  32. The Use of Conjugate Priors with Latent Variable Models
  33. Mixture of Conjugate Priors
  34. Renormalized Conjugate Distributions
  35. Discussion: To Be or Not To Be Conjugate?
  36. Summary
  37. Priors Over Multinomial and Categorical Distributions
  38. The Dirichlet Distribution Re-visited
  39. The Logistic Normal Distribution
  40. Discussion
  41. Summary
  42. Non-informative Priors
  43. Uniform and Improper Priors
  44. Jeffreys Prior
  45. Discussion
  46. Conjugacy and Exponential Models
  47. Multiple Parameter Draws in Models
  48. Structural Priors
  49. Conclusion and Summary
  50. Exercises
  51. Bayesian Estimation
  52. Learning with Latent Variables: Two Views
  53. Bayesian Point Estimation
  54. Maximum a Posteriori Estimation
  55. Posterior Approximations Based on the MAP Solution
  56. Decision-theoretic Point Estimation
  57. Discussion and Summary
  58. Empirical Bayes
  59. Asymptotic Behavior of the Posterior
  60. Summary
  61. Exercises
  62. Sampling Methods
  63. MCMC Algorithms: Overview
  64. NLP Model Structure for MCMC Inference
  65. Partitioning the Latent Variables
  66. Gibbs Sampling
  67. Collapsed Gibbs Sampling
  68. Operator View
  69. Parallelizing the Gibbs Sampler
  70. Summary
  71. The Metropolis-Hastings Algorithm
  72. Variants of Metropolis-Hastings
  73. Slice Sampling
  74. Auxiliary Variable Sampling
  75. The Use of Slice Sampling and Auxiliary Variable Sampling in NLP
  76. Simulated Annealing
  77. Convergence of MCMC Algorithms
  78. Markov Chain: Basic Theory
  79. Sampling Algorithms Not in the MCMC Realm
  80. Monte Carlo Integration
  81. Discussion
  82. Computability of Distribution vs. Sampling
  83. Nested MCMC Sampling
  84. Runtime of MCMC Samplers
  85. Particle Filtering
  86. Conclusion and Summary
  87. Exercises
  88. Variational Inference
  89. Variational Bound on Marginal Log-likelihood
  90. Mean-field Approximation
  91. Mean-field Variational Inference Algorithm
  92. Dirichlet-multinomial Variational Inference
  93. Connection to the Expectation-maximization Algorithm
  94. Empirical Bayes with Variational Inference
  95. Discussion
  96. Initialization of the Inference Algorithms
  97. Convergence Diagnosis
  98. The Use of Variational Inference for Decoding
  99. Variational Inference as KL Divergence Minimization
  100. Online Variational Inference
  101. Summary
  102. Exercises
  103. Nonparametric Priors
  104. The Dirichlet Process: Three Views
  105. The Stick-breaking Process
  106. The Chinese Restaurant Process
  107. Dirichlet Process Mixtures
  108. Inference with Dirichlet Process Mixtures
  109. Dirichlet Process Mixture as a Limit of Mixture Models
  110. The Hierarchical Dirichlet Process
  111. The Pitman-Yor Process
  112. Pitman-Yor Process for Language Modeling
  113. Power-law Behavior of the Pitman-Yor Process
  114. Discussion
  115. Gaussian Processes
  116. The Indian Buffet Process
  117. Nested Chinese Restaurant Process
  118. Distance-dependent Chinese Restaurant Process
  119. Sequence Memoizers
  120. Summary
  121. Exercises
  122. Bayesian Grammar Models
  123. Bayesian Hidden Markov Models
  124. Hidden Markov Models with an Infinite State Space
  125. Probabilistic Context-free Grammars
  126. PCFGs as a Collection of Multinomials
  127. Basic Inference Algorithms for PCFGs
  128. Hidden Markov Models as PCFGs
  129. Bayesian Probabilistic Context-free Grammars
  130. Priors on PCFGs
  131. Monte Carlo Inference with Bayesian PCFGs
  132. Variational Inference with Bayesian PCFGs
  133. Adaptor Grammars
  134. Pitman-Yor Adaptor Grammars
  135. Stick-breaking View of PYAG
  136. Inference with PYAG
  137. Hierarchical Dirichlet Process PCFGs (HDP-PCFGs)
  138. Extensions to the HDP-PCFG Model
  139. Dependency Grammars
  140. State-split Nonparametric Dependency Models
  141. Synchronous Grammars
  142. Multilingual Learning
  143. Part-of-speech Tagging
  144. Grammar Induction
  145. Further Reading
  146. Summary
  147. Exercises
  148. Closing Remarks
  149. Basic Concepts
  150. Basic Concepts in Information Theory
  151. Entropy and Cross Entropy
  152. Kullback-Leibler Divergence
  153. Other Basic Concepts
  154. Jensen’s Inequality
  155. Transformation of Continuous Random Variables
  156. The Expectation-maximization Algorithm
  157. Distribution Catalog
  158. The Multinomial Distribution
  159. The Dirichlet Distribution
  160. The Poisson Distribution
  161. The Gamma Distribution
  162. The Multivariate Normal Distribution
  163. The Laplace Distribution
  164. The Logistic Normal Distribution
  165. The Inverse Wishart Distribution

People also search for Bayesian Analysis in Natural Language Processing 2nd:

bayesian analysis in natural language processing
    
bayesian analysis in natural language processing pdf
    
bayesian nlp
    
bayesian language model
    
bayesian language

Tags: Shay Cohen, Analysis, Natural Language