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Deep Learning And Linguistic Representation

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Deep Learning And Linguistic Representation

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Author : Shalom Lappin
language : en
Publisher: CRC Press
Release Date : 2021-04-27

Deep Learning And Linguistic Representation written by Shalom Lappin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with Computers categories.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

Deep Learning And Linguistic Representation

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Author : Shalom Lappin
language : en
Publisher: CRC Press
Release Date : 2021-04-26

The Philosophy Of Theoretical Linguistics

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Author : Ryan M. Nefdt
language : en
Publisher: Cambridge University Press
Release Date : 2024-05-02

The Philosophy Of Theoretical Linguistics written by Ryan M. Nefdt and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Language Arts & Disciplines categories.

What is the remit of theoretical linguistics? How are human languages different from animal calls or artificial languages? What philosophical insights about language can be gleaned from phonology, pragmatics, probabilistic linguistics, and deep learning? This book addresses the current philosophical issues at the heart of theoretical linguistics, which are widely debated not only by linguists, but also philosophers, psychologists, and computer scientists. It delves into hitherto uncharted territory, putting philosophy in direct conversation with phonology, sign language studies, supersemantics, computational linguistics, and language evolution. A range of theoretical positions are covered, from optimality theory and autosegmental phonology to generative syntax, dynamic semantics, and natural language processing with deep learning techniques. By both unwinding the complexities of natural language and delving into the nature of the science that studies it, this book ultimately improves our tools of discovery aimed at one of the most essential features of our humanity, our language.

Representation Learning For Natural Language Processing

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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2020-07-03

Representation Learning For Natural Language Processing written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Computers categories.

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Algebraic Structures In Natural Language

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Author : Shalom Lappin
language : en
Publisher: CRC Press
Release Date : 2022-12-23

Algebraic Structures In Natural Language written by Shalom Lappin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-23 with Computers categories.

Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction and information theory, rather than on formal models of grammar induction. This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioral science and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language. The text represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between computational models and natural language.

Building Machine Learning And Deep Learning Models On Google Cloud Platform

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Author : Ekaba Bisong
language : en
Publisher: Apress
Release Date : 2019-09-27

Building Machine Learning And Deep Learning Models On Google Cloud Platform written by Ekaba Bisong and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-27 with Computers categories.

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is dividedinto eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results Know the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Deep Learning For Nlp And Speech Recognition

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Author : Uday Kamath
language : en
Publisher: Springer
Release Date : 2019-06-10

Deep Learning For Nlp And Speech Recognition written by Uday Kamath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-10 with Computers categories.

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

Deep Learning Approaches To Text Production

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Author : Shashi Narayan
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-03-20

Deep Learning Approaches To Text Production written by Shashi Narayan and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-20 with Computers categories.

Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.

Deep Learning

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Author : Shuhao Wang
language : en
Publisher: Elsevier
Release Date : 2025-07-25

Deep Learning written by Shuhao Wang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Technology & Engineering categories.

Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligenceThe code for book may be accessed by visiting the companion website: https://www.elsevier.com/books-and-journals/book-companion/9780443439544 - Provides in-depth explanations and practical code examples for the latest deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers - Examines theoretical concepts and the engineering practices required for deploying deep learning models in real-world scenarios - Covers the use of distributed systems for training and deploying models - Includes detailed case studies and applications of deep learning models in various domains including image classification, object detection, and semantic segmentation

The Science Of Deep Learning

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Author : Iddo Drori
language : en
Publisher: Cambridge University Press
Release Date : 2022-08-18

The Science Of Deep Learning written by Iddo Drori and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-18 with Computers categories.

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.

Deep Learning And Linguistic Representation

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