Bayesian Analysis in Natural Language Processing, Second Edition
eBook - PDF

Bayesian Analysis in Natural Language Processing, Second Edition

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Bayesian Analysis in Natural Language Processing, Second Edition

About this book

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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Yes, you can access Bayesian Analysis in Natural Language Processing, Second Edition by Shay Cohen in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Copyright Page
  3. Title Page
  4. Dedication
  5. Contents
  6. List of Figures
  7. List of Algorithms
  8. List of Generative Stories
  9. Preface (First Edition)
  10. Acknowledgments (First Edition)
  11. Preface (Second Edition)
  12. Preliminaries
  13. Introduction
  14. Priors
  15. Bayesian Estimation
  16. Sampling Methods
  17. Variational Inference
  18. Nonparametric Priors
  19. Bayesian Grammar Models
  20. Representation Learning and Neural Networks
  21. Closing Remarks
  22. Basic Concepts
  23. Distribution Catalog
  24. Bibliography
  25. Author's Biography
  26. Index