Neural Models Of Language Processes

Author : Michael Arbib
Publisher : Academic Press
Page : 592 pages
File Size : 54,8 Mb
Release : 2012-12-02
Category : Medical
ISBN : 9780323140812

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Neural Models of language Processes by Michael Arbib Pdf

Neural Models of Language Processes offers an interdisciplinary approach to understanding the nature of human language and the means whereby we use it. The book is organized into five parts. Part I provides an opening framework that addresses three tasks: to place neurolinguistics in current perspective; to provide two case studies of aphasia; and to discuss the ""rules of the game"" of the various disciplines that contribute to this volume. Part II on artificial intelligence (AI) and processing models discusses the contribution of AI to neurolinguistics. The chapters in this section introduce three AI systems for language perception: the HWIM and HEARSAY systems that proceed from an acoustic input to a semantic interpretation of the utterance it represents, and Marcus9 system for parsing sentences presented in text. Studying these systems demonstrates the virtues of implemented or implementable models. Part III on linguistic and psycholinguistic perspectives includes studies such as nonaphasic language behavior and the linguistics and psycholinguistics of sign language. Part IV examines neurological perspectives such as the neuropathological basis of Broca's aphasia and the simulation of speech production without a computer. Part V on neuroscience and brain theory includes studies such as the histology, architectonics, and asymmetry of language areas; hierarchy and evolution in neurolinguistics; and perceptual-motor processes and the neural basis of language.

Author : Yoav Goldberg
Publisher : Springer Nature
Page : 20 pages
File Size : 42,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031021657

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Neural Network Methods for Natural Language Processing by Yoav Goldberg Pdf

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Author : S., Sumathi,M., Janani
Publisher : IGI Global
Page : 227 pages
File Size : 55,6 Mb
Release : 2019-11-29
Category : Computers
ISBN : 9781799811619

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Neural Networks for Natural Language Processing by S., Sumathi,M., Janani Pdf

Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.

Author : Yoav Goldberg
Publisher : Morgan & Claypool Publishers
Page : 311 pages
File Size : 47,8 Mb
Release : 2017-04-17
Category : Computers
ISBN : 9781627052955

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Neural Network Methods in Natural Language Processing by Yoav Goldberg Pdf

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Author : Jose Manuel Gomez-Perez,Ronald Denaux,Andres Garcia-Silva
Publisher : Springer Nature
Page : 268 pages
File Size : 47,7 Mb
Release : 2020-06-16
Category : Computers
ISBN : 9783030448301

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A Practical Guide to Hybrid Natural Language Processing by Jose Manuel Gomez-Perez,Ronald Denaux,Andres Garcia-Silva Pdf

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks. Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment. A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.

Author : David Caplan,André Roch Lecours,Alan Smith
Publisher : MIT Press
Page : 436 pages
File Size : 40,5 Mb
Release : 1984
Category : Medical
ISBN : 0262031019

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Biological Perspectives on Language by David Caplan,André Roch Lecours,Alan Smith Pdf

Profoundly influenced by the analyses, of contemporary linguistics, these original contributions bring a number of different views to bear on important issues in a controversial area of study. The linguistic structures and language-related processes the book deals with are for the most part central (syntactic structures, phonological representations, semantic readings) rather than peripheral (acousticphonetic structures and the perception and production of these structures) aspects of language. Each section contains a summarizing introduction. Section I takes up issues at the interface of linguistics and neurology: The Concept of a Mental Organ for Language; Neural Mechanisms, Aphasia, and Theories of Language; Brain-based and Non-brain-based Models of Language; Vocal Learning and Its Relation to Replaceable Synapses and Neurons. Section II presents linguistic and psycholinguistic issues: Aspects of Infant Competence and the Acquisition of Language; the Linguistic Analysis of Aphasic Syndromes; the Clinical Description of Aphasia (Linguistic Aspects); The Psycholinguistic Interpretation of Aphasias; The Organization of Processing Structure for Language Production; and The Neuropsychology of Bilingualism. Section III deals with neural issues: Where is the Speech Area and Who has Seen It? Determinants of Recovery from Aphasia; Anatomy of Language; Lessons from Comparative Anatomy; Event Related Potentials and Language; Neural Models and Very Little About Language. David Caplan, M.D. edited Biological Studies of Mental Processes(MIT Press 1980), and is a member of the editorial staff of two prestigious journals, Cognition and Brain & Behavorial Sciences, He works at the Montreal Neurological Institute. Andreacute; Roch Lecours is Professor of Neurology and Allan Smith Professor of Physiology, both at the University of Montreal. The book is in the series, Studies in Neuropsychology and Neurolinguistics.

Author : Palash Goyal,Sumit Pandey,Karan Jain
Publisher : Apress
Page : 290 pages
File Size : 44,6 Mb
Release : 2018-06-26
Category : Computers
ISBN : 9781484236857

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Deep Learning for Natural Language Processing by Palash Goyal,Sumit Pandey,Karan Jain Pdf

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Author : Gerry T. M. Altmann
Publisher : Psychology Press
Page : 436 pages
File Size : 55,8 Mb
Release : 1997
Category : Computational linguistics
ISBN : 0863779751

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Cognitive Models of Speech Processing by Gerry T. M. Altmann Pdf

This collection of papers and abstracts stems from the third meeting in the series of Sperlonga workshops on Cognitive Models of Speech Processing. It presents current research on the structure and organization of the mental lexicon, and on the processes that access that lexicon. The volume starts with discussion of issues in acquisition and consideration of questions such as, 'What is the relationship between vocabulary growth and the acquisition of syntax?', and, 'How does prosodic information, concerning the melodies and rhythms of the language, influence the processes of lexical and syntactic acquisition?'. From acquisition, the papers move on to consider the manner in which contemporary models of spoken word recognition and production can map onto neural models of the recognition and production processes. The issue of exactly what is recognised, and when, is dealt with next - the empirical findings suggest that the function of something to which a word refers is accessed with a different time-course to the form of that something. This has considerable implications for the nature, and content, of lexical representations. Equally important are the findings from the studies of disordered lexical processing, and two papers in this volume address the implications of these disorders for models of lexical representation and process (borrowing from both empirical data and computational modelling). The final paper explores whether neural networks can successfully model certain lexical phenomena that have elsewhere been assumed to require rule-based processes.

Author : Gerry Altmann
Publisher : Psychology Press
Page : 128 pages
File Size : 54,9 Mb
Release : 2018-01-24
Category : Electronic
ISBN : 1138883115

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Cognitive Models of Speech Processing by Gerry Altmann Pdf

This collection of papers and abstracts stems from the third meeting in the series of Sperlonga workshops on Cognitive Models of Speech Processing. It presents current research on the structure and organization of the mental lexicon, and on the processes that access that lexicon. The volume starts with discussion of issues in acquisition and consideration of questions such as, 'What is the relationship between vocabulary growth and the acquisition of syntax?', and, 'How does prosodic information, concerning the melodies and rhythms of the language, influence the processes of lexical and syntactic acquisition?'. From acquisition, the papers move on to consider the manner in which contemporary models of spoken word recognition and production can map onto neural models of the recognition and production processes. The issue of exactly what is recognised, and when, is dealt with next - the empirical findings suggest that the function of something to which a word refers is accessed with a different time-course to the form of that something. This has considerable implications for the nature, and content, of lexical representations. Equally important are the findings from the studies of disordered lexical processing, and two papers in this volume address the implications of these disorders for models of lexical representation and process (borrowing from both empirical data and computational modelling). The final paper explores whether neural networks can successfully model certain lexical phenomena that have elsewhere been assumed to require rule-based processes.

Author : Yue Zhang,Zhiyang Teng
Publisher : Cambridge University Press
Page : 487 pages
File Size : 42,7 Mb
Release : 2021-01-07
Category : Computers
ISBN : 9781108420211

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Natural Language Processing by Yue Zhang,Zhiyang Teng Pdf

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.

Author : Lisa Beinborn,Nora Hollenstein
Publisher : Springer Nature
Page : 166 pages
File Size : 40,9 Mb
Release : 2023-12-04
Category : Computers
ISBN : 9783031432606

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Cognitive Plausibility in Natural Language Processing by Lisa Beinborn,Nora Hollenstein Pdf

This book explores the cognitive plausibility of computational language models and why it’s an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research.

Author : Bernd J. Kröger,Trevor Bekolay
Publisher : Springer
Page : 280 pages
File Size : 46,9 Mb
Release : 2019-07-11
Category : Medical
ISBN : 9783030158538

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Neural Modeling of Speech Processing and Speech Learning by Bernd J. Kröger,Trevor Bekolay Pdf

This book explores the processes of spoken language production and perception from a neurobiological perspective. After presenting the basics of speech processing and speech acquisition, a neurobiologically-inspired and computer-implemented neural model is described, which simulates the neural processes of speech processing and speech acquisition. This book is an introduction to the field and aimed at students and scientists in neuroscience, computer science, medicine, psychology and linguistics.

Author : Dan Jurafsky
Publisher : Pearson Education India
Page : 912 pages
File Size : 48,8 Mb
Release : 2000-09
Category : Electronic
ISBN : 8131716724

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Speech & Language Processing by Dan Jurafsky Pdf

Author : Dawn E. Holmes
Publisher : Springer
Page : 276 pages
File Size : 40,6 Mb
Release : 2006-02-28
Category : Technology & Engineering
ISBN : 9783540334866

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Innovations in Machine Learning by Dawn E. Holmes Pdf

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

Author : Lyndon White,Roberto Togneri,Wei Liu,Mohammed Bennamoun
Publisher : Springer
Page : 122 pages
File Size : 42,8 Mb
Release : 2018-08-29
Category : Technology & Engineering
ISBN : 9789811300622

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Neural Representations of Natural Language by Lyndon White,Roberto Togneri,Wei Liu,Mohammed Bennamoun Pdf

This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.