Bayesian Analysis in Natural Language Processing
Bayesian Analysis in Natural Language Processing

Author: Shay Cohen

Publisher: Morgan & Claypool Publishers

Published: 2016-06-01

Total Pages: 276

ISBN-13: 1627054219

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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
Bayesian Analysis in Natural Language Processing

Author: Shay Cohen

Publisher: Morgan & Claypool Publishers

Published: 2019-04-09

Total Pages: 345

ISBN-13: 168173527X

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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.

Bayesian Analysis in Natural Language Processing
Bayesian Analysis in Natural Language Processing

Author: Shay Cohen

Publisher: Springer Nature

Published: 2022-11-10

Total Pages: 266

ISBN-13: 3031021614

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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, Second Edition
Bayesian Analysis in Natural Language Processing, Second Edition

Author: Shay Cohen

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 311

ISBN-13: 3031021703

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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.

Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks

Author: Adnan Darwiche

Publisher: Cambridge University Press

Published: 2009-04-06

Total Pages: 561

ISBN-13: 0521884381

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This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Foundations of Statistical Natural Language Processing
Foundations of Statistical Natural Language Processing

Author: Christopher Manning

Publisher: MIT Press

Published: 1999-05-28

Total Pages: 719

ISBN-13: 0262303795

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Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Linguistic Fundamentals for Natural Language Processing II
Linguistic Fundamentals for Natural Language Processing II

Author: Emily M. Bender

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 250

ISBN-13: 303102172X

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Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG). This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.

MACHINE LEARNING FOR NATURAL LANGUAGE PROCESSING: INSIGHTS INTO TEXT AND SPEECH ANALYSIS
MACHINE LEARNING FOR NATURAL LANGUAGE PROCESSING: INSIGHTS INTO TEXT AND SPEECH ANALYSIS

Author: Mr. Harish Reddy Gantla

Publisher: Xoffencerpublication

Published: 2024-05-16

Total Pages: 236

ISBN-13: 8197370834

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The fourth industrial revolution, according to the World Economic Forum, is about to begin. This will blend the physical and digital worlds in ways we couldn’t imagine a few years ago. Advances in machine learning and AI will help usher in these existing changes. Machine learning is transformative which opens up new scenarios that were simply impossible a few years ago. Profound gaining addresses a significant change in perspective from customary programming improvement models. Instead of having to write explicit top-down instructions for how software should behave, deep learning allows your software to generalize rules of operations. Deep learning models empower the engineers to configure, characterized by the information without the guidelines to compose. Deep learning models are conveyed at scale and creation applications—for example, car, gaming, medical services, and independent vehicles. Deep learning models employ artificial neural networks, which are computer architectures comprising multiple layers of interconnected components. By avoiding data transmission through these connected units, a neural network can learn how to approximate the computations required to transform inputs to outputs. Deep learning models require top-notch information to prepare a brain organization to carry out a particular errand. Contingent upon your expected applications, you might have to get thousands to millions of tests. This chapter takes you on a journey of AI from where it got originated. It does not just involve the evolution of computer science, but it involves several fields say biology, statistics, and probability. Let us start its span from biological neurons; way back in 1871, Joseph von Gerlach proposed the reticulum theory, which asserted that “the nervous system is a single continuous network rather than a network of numerous separate cells.” According to him, our human nervous system is a single system and not a network of discrete cells. Camillo Golgi was able to examine neural tissues in greater detail than ever before, thanks to a chemical reaction he discovered. He concluded that the human nervous system was composed of a single cell and reaffirmed his support for the reticular theory. In 1888, Santiago Ramon y Cajal used Golgi’s method to examine the nervous system and concluded that it is a collection of distinct cells rather than a single cell.

Multilingual Natural Language Processing Applications
Multilingual Natural Language Processing Applications

Author: Daniel Bikel

Publisher: IBM Press

Published: 2012-05-11

Total Pages: 829

ISBN-13: 0137047819

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Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today’s best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others. Coverage includes Core NLP problems, and today’s best algorithms for attacking them Processing the diverse morphologies present in the world’s languages Uncovering syntactical structure, parsing semantics, using semantic role labeling, and scoring grammaticality Recognizing inferences, subjectivity, and opinion polarity Managing key algorithmic and design tradeoffs in real-world applications Extracting information via mention detection, coreference resolution, and events Building large-scale systems for machine translation, information retrieval, and summarization Answering complex questions through distillation and other advanced techniques Creating dialog systems that leverage advances in speech recognition, synthesis, and dialog management Constructing common infrastructure for multiple multilingual text processing applications This book will be invaluable for all engineers, software developers, researchers, and graduate students who want to process large quantities of text in multiple languages, in any environment: government, corporate, or academic.