Download or read book Neural Models of language Processes written by Michael Arbib and published by Academic Press. This book was released on 2012-12-02 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Neural Network Methods for Natural Language Processing written by Yoav Goldberg and published by Springer Nature. This book was released on 2022-06-01 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Neural Networks for Natural Language Processing written by S., Sumathi and published by IGI Global. This book was released on 2019-11-29 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Neural Network Methods in Natural Language Processing written by Yoav Goldberg and published by Morgan & Claypool Publishers. This book was released on 2017-04-17 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Learning for Natural Language Processing written by Palash Goyal and published by Apress. This book was released on 2018-06-26 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Incorporating Structure Into Neural Models for Language Processing written by Michael Schlichtkrull and published by . This book was released on 2021 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Mechanisms of Language written by Maria Mody and published by Springer. This book was released on 2017-10-24 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important volume brings together significant findings on the neural bases of spoken language –its processing, use, and organization, including its phylogenetic roots. Employing a potent mix of conceptual and neuroimaging-based approaches, contributors delve deeply into specialized structures of the speech system, locating sensory and cognitive mechanisms involved in listening and comprehension, grasping meanings and storing memories. The novel perspectives revise familiar models by tracing linguistic interactions within and between neural systems, homing in on the brain’s semantic network, exploring the neuroscience behind bilingualism and multilingual fluency, and even making a compelling case for a more nuanced participation of the motor system in speech. From these advances, readers have a more three-dimensional picture of the brain—its functional epicenters, its connections, and the whole—as the seat of language in both wellness and disorders. Included in the topics: · The interaction between storage and computation in morphosyntactic processing. · The role of language in structure-dependent cognition. · Multisensory integration in speech processing: neural mechanisms of cross-modal after-effect. · A neurocognitive view of the bilingual brain. · Causal modeling: methods and their application to speech and language. · A word in the hand: the gestural origins of language. Neural Mechanisms of Language presents a sophisticated mix of detail and creative approaches to understanding brain structure and function, giving neuropsychologists, cognitive neuroscientists, developmental psychologists, cognitive psychologists, and speech/language pathologists new windows onto the research shaping their respective fields.
Download or read book A Practical Guide to Hybrid Natural Language Processing written by Jose Manuel Gomez-Perez and published by Springer Nature. This book was released on 2020-06-16 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Biological Perspectives on Language written by David Caplan and published by MIT Press. This book was released on 1984 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Hierarchy and Interpretability in Neural Models of Language Processing written by Dieuwke Hupkes and published by . This book was released on 2020 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neurobiology of Language written by Gregory Hickok and published by Academic Press. This book was released on 2015-08-15 with total page 1188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neurobiology of Language explores the study of language, a field that has seen tremendous progress in the last two decades. Key to this progress is the accelerating trend toward integration of neurobiological approaches with the more established understanding of language within cognitive psychology, computer science, and linguistics. This volume serves as the definitive reference on the neurobiology of language, bringing these various advances together into a single volume of 100 concise entries. The organization includes sections on the field's major subfields, with each section covering both empirical data and theoretical perspectives. "Foundational" neurobiological coverage is also provided, including neuroanatomy, neurophysiology, genetics, linguistic, and psycholinguistic data, and models. - Foundational reference for the current state of the field of the neurobiology of language - Enables brain and language researchers and students to remain up-to-date in this fast-moving field that crosses many disciplinary and subdisciplinary boundaries - Provides an accessible entry point for other scientists interested in the area, but not actively working in it – e.g., speech therapists, neurologists, and cognitive psychologists - Chapters authored by world leaders in the field – the broadest, most expert coverage available
Download or read book Speech Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Linguistics for the Age of AI written by Marjorie Mcshane and published by MIT Press. This book was released on 2021-03-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.
Download or read book Cognitive Models of Speech Processing written by Gerry T. M. Altmann and published by Psychology Press. This book was released on 1997 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Cognitive Plausibility in Natural Language Processing written by Lisa Beinborn and published by Springer Nature. This book was released on 2023-12-04 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Neural Representations of Natural Language written by Lyndon White and published by Springer. This book was released on 2018-08-29 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Learning in Natural Language Processing written by Li Deng and published by Springer. This book was released on 2018-05-23 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
