Neural Models Of Language Processes PDF Download
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Author | : Michael Arbib |
Publisher | : Academic Press |
Total Pages | : 592 |
Release | : 2012-12-02 |
Genre | : Medical |
ISBN | : 0323140815 |
Download Neural Models of language Processes Book in PDF, ePub and Kindle
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 |
Total Pages | : 20 |
Release | : 2022-06-01 |
Genre | : Computers |
ISBN | : 3031021657 |
Download Neural Network Methods for Natural Language Processing Book in PDF, ePub and Kindle
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 |
Publisher | : IGI Global |
Total Pages | : 227 |
Release | : 2019-11-29 |
Genre | : Computers |
ISBN | : 1799811611 |
Download Neural Networks for Natural Language Processing Book in PDF, ePub and Kindle
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 | : Michael Schlichtkrull |
Publisher | : |
Total Pages | : 140 |
Release | : 2021 |
Genre | : |
ISBN | : |
Download Incorporating Structure Into Neural Models for Language Processing Book in PDF, ePub and Kindle
Author | : Yoav Goldberg |
Publisher | : Morgan & Claypool Publishers |
Total Pages | : 370 |
Release | : 2017-04-17 |
Genre | : Computers |
ISBN | : 168173155X |
Download Neural Network Methods in Natural Language Processing Book in PDF, ePub and Kindle
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 | : Palash Goyal |
Publisher | : Apress |
Total Pages | : 290 |
Release | : 2018-06-26 |
Genre | : Computers |
ISBN | : 1484236858 |
Download Deep Learning for Natural Language Processing Book in PDF, ePub and Kindle
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 | : Jose Manuel Gomez-Perez |
Publisher | : Springer Nature |
Total Pages | : 268 |
Release | : 2020-06-16 |
Genre | : Computers |
ISBN | : 3030448304 |
Download A Practical Guide to Hybrid Natural Language Processing Book in PDF, ePub and Kindle
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 |
Publisher | : MIT Press |
Total Pages | : 436 |
Release | : 1984 |
Genre | : Medical |
ISBN | : 9780262031011 |
Download Biological Perspectives on Language Book in PDF, ePub and Kindle
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 | : Dieuwke Hupkes |
Publisher | : |
Total Pages | : 183 |
Release | : 2020 |
Genre | : |
ISBN | : 9789064022227 |
Download Hierarchy and Interpretability in Neural Models of Language Processing Book in PDF, ePub and Kindle
Author | : Dan Jurafsky |
Publisher | : Pearson Education India |
Total Pages | : 912 |
Release | : 2000-09 |
Genre | : |
ISBN | : 9788131716724 |
Download Speech & Language Processing Book in PDF, ePub and Kindle