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Author: Yoav Goldberg
Publisher: Springer Nature
ISBN: 3031021657
Category : Computers
Languages : en
Pages : 20
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Book Description
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: Yoav Goldberg
Publisher: Springer Nature
ISBN: 3031021657
Category : Computers
Languages : en
Pages : 20
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Book Description
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
ISBN: 1799811611
Category : Computers
Languages : en
Pages : 227
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Book Description
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 Arbib
Publisher: Academic Press
ISBN: 0323140815
Category : Medical
Languages : en
Pages : 592
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Book Description
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: Palash Goyal
Publisher: Apress
ISBN: 1484236858
Category : Computers
Languages : en
Pages : 290
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Book Description
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
ISBN: 9783030448325
Category : Computers
Languages : en
Pages : 268
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Book Description
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: Marjorie Mcshane
Publisher: MIT Press
ISBN: 0262362600
Category : Computers
Languages : en
Pages : 449
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Book Description
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.
Author: Lyndon White
Publisher: Springer
ISBN: 9811300623
Category : Technology & Engineering
Languages : en
Pages : 132
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Book Description
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.
Author: Dan Jurafsky
Publisher: Pearson Education India
ISBN: 9788131716724
Category :
Languages : en
Pages : 912
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Book Description
Author: Maria Mody
Publisher: Springer
ISBN: 1493973258
Category : Psychology
Languages : en
Pages : 227
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Book Description
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.
Author: Jerome Feldman
Publisher: MIT Press
ISBN: 0262562359
Category : Psychology
Languages : en
Pages : 379
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Book Description
In From Molecule to Metaphor, Jerome Feldman proposes a theory of language and thought that treats language not as an abstract symbol system but as a human biological ability that can be studied as a function of the brain, as vision and motor control are studied. This theory, he writes, is a "bridging theory" that works from extensive knowledge at two ends of a causal chain to explicate the links between. Although the cognitive sciences are revealing much about how our brains produce language and thought, we do not yet know exactly how words are understood or have any methodology for finding out. Feldman develops his theory in computer simulations—formal models that suggest ways that language and thought may be realized in the brain. Combining key findings and theories from biology, computer science, linguistics, and psychology, Feldman synthesizes a theory by exhibiting programs that demonstrate the required behavior while remaining consistent with the findings from all disciplines. After presenting the essential results on language, learning, neural computation, the biology of neurons and neural circuits, and the mind/brain, Feldman introduces specific demonstrations and formal models of such topics as how children learn their first words, words for abstract and metaphorical concepts, understanding stories, and grammar (including "hot-button" issues surrounding the innateness of human grammar). With this accessible, comprehensive book Feldman offers readers who want to understand how our brains create thought and language a theory of language that is intuitively plausible and also consistent with existing scientific data at all levels.
