Bayesian analysis in natural language processing 2nd Edition by Cohen S 1681735269 9781681735269

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ISBN-10 :  1681735269 

ISBN-13 :  9781681735269

Author:  Cohen S 

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.

 

Bayesian analysis in natural language processing 2nd Table of contents:

1. List of Figures
2. List of Figures
3. List of Figures
4. Preface 
5. Acknowledgments 
6. Preface 
7. Probability Measures
8. Random Variables
9. Continuous and Discrete Random Variables
10. Joint Distribution over Multiple Random Variables
11. Conditional Distributions
12. Bayes’ Rule
13. Independent and Conditionally Independent Random Variables
14. Exchangeable Random Variables
15. Expectations of Random Variables
16. Parametric vs. Nonparametric Models
17. Inference with Models
18. Generative Models
19. Independence Assumptions in Models
20. Directed Graphical Models
21. Learning from Data Scenarios
22. Bayesian and Frequentist Philosophy (Tip of the Iceberg)
23. Summary
24. Exercises
25. Introduction

 

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Tags: Bayesian analysis, natural language, processing, Cohen