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Deep Learning and Linguistic Representation

Shalom Lappin

Author : Shalom Lappin
Publisher : CRC Press
Page : 162 pages
File Size : 44,9 Mb
Release : 2021-04-26
Category : Computers
ISBN : 9781000380323

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Deep Learning and Linguistic Representation by Shalom Lappin Pdf

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.

Neural Representations of Natural Language

Lyndon White,Roberto Togneri,Wei Liu,Mohammed Bennamoun

Author : Lyndon White,Roberto Togneri,Wei Liu,Mohammed Bennamoun
Publisher : Springer
Page : 122 pages
File Size : 49,9 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.

Representation Learning for Natural Language Processing

Zhiyuan Liu,Yankai Lin,Maosong Sun

Author : Zhiyuan Liu,Yankai Lin,Maosong Sun
Publisher : Springer Nature
Page : 319 pages
File Size : 54,5 Mb
Release : 2020-07-03
Category : Computers
ISBN : 9789811555732

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Representation Learning for Natural Language Processing by Zhiyuan Liu,Yankai Lin,Maosong Sun Pdf

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.

Deep Learning and Linguistic Representation

Shalom Lappin

Author : Shalom Lappin
Publisher : CRC Press
Page : 131 pages
File Size : 40,5 Mb
Release : 2021-04-27
Category : Computers
ISBN : 9781000380330

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Deep Learning and Linguistic Representation by Shalom Lappin Pdf

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 Approaches to Text Production

Shashi Narayan,Claire Gardent

Author : Shashi Narayan,Claire Gardent
Publisher : Morgan & Claypool Publishers
Page : 201 pages
File Size : 51,5 Mb
Release : 2020-03-20
Category : Computers
ISBN : 9781681737591

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Deep Learning Approaches to Text Production by Shashi Narayan,Claire Gardent Pdf

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 in Natural Language Processing

Li Deng,Yang Liu

Author : Li Deng,Yang Liu
Publisher : Springer
Page : 329 pages
File Size : 52,7 Mb
Release : 2018-05-23
Category : Computers
ISBN : 9789811052095

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Deep Learning in Natural Language Processing by Li Deng,Yang Liu Pdf

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.

Embeddings in Natural Language Processing

Mohammad Taher Pilehvar,Jose Camacho-Collados

Author : Mohammad Taher Pilehvar,Jose Camacho-Collados
Publisher : Morgan & Claypool Publishers
Page : 177 pages
File Size : 52,9 Mb
Release : 2020-11-13
Category : Computers
ISBN : 9781636390222

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Embeddings in Natural Language Processing by Mohammad Taher Pilehvar,Jose Camacho-Collados Pdf

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.

Modelling with Words

Jonathan Lawry,Jimi Shanahan,Anca Ralescu

Author : Jonathan Lawry,Jimi Shanahan,Anca Ralescu
Publisher : Springer
Page : 506 pages
File Size : 50,9 Mb
Release : 2003-10-28
Category : Computers
ISBN : 9783540399063

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Modelling with Words by Jonathan Lawry,Jimi Shanahan,Anca Ralescu Pdf

Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - scaling linguistic algorithms to high-dimensional data problems - integrating linguistic expert knowledge with knowledge derived from data - identifying sound and useful inference rules - integrating fuzzy and probabilistic uncertainty in data modelling

Representation Learning for Natural Language Processing

Zhiyuan Liu,Yankai Lin,Maosong Sun

Author : Zhiyuan Liu,Yankai Lin,Maosong Sun
Publisher : Springer
Page : 334 pages
File Size : 49,5 Mb
Release : 2020-09-08
Category : Computers
ISBN : 9811555729

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Representation Learning for Natural Language Processing by Zhiyuan Liu,Yankai Lin,Maosong Sun Pdf

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.

Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision

L. Ashok Kumar,D. Karthika Renuka

Author : L. Ashok Kumar,D. Karthika Renuka
Publisher : CRC Press
Page : 251 pages
File Size : 44,7 Mb
Release : 2023-05-22
Category : Business & Economics
ISBN : 9781000875607

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Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision by L. Ashok Kumar,D. Karthika Renuka Pdf

Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of natural language processing (NLP), speech, and computer vision tasks. It simplifies and presents the concepts of deep learning in a comprehensive manner, with suitable, full-fledged examples of deep learning models, with an aim to bridge the gap between the theoretical and the applications using case studies with code, experiments, and supporting analysis. Features: Covers latest developments in deep learning techniques as applied to audio analysis, computer vision, and natural language processing. Introduces contemporary applications of deep learning techniques as applied to audio, textual, and visual processing. Discovers deep learning frameworks and libraries for NLP, speech, and computer vision in Python. Gives insights into using the tools and libraries in Python for real-world applications. Provides easily accessible tutorials and real-world case studies with code to provide hands-on experience. This book is aimed at researchers and graduate students in computer engineering, image, speech, and text processing.

Embeddings in Natural Language Processing

Mohammad Taher Pilehvar,Jose Camacho-Collados

Author : Mohammad Taher Pilehvar,Jose Camacho-Collados
Publisher : Springer Nature
Page : 157 pages
File Size : 52,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021770

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Embeddings in Natural Language Processing by Mohammad Taher Pilehvar,Jose Camacho-Collados Pdf

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.

Neural Network Methods for Natural Language Processing

Yoav Goldberg

Author : Yoav Goldberg
Publisher : Springer Nature
Page : 20 pages
File Size : 44,8 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.

Deep Learning for Natural Language Processing

Palash Goyal,Sumit Pandey,Karan Jain

Author : Palash Goyal,Sumit Pandey,Karan Jain
Publisher : Apress
Page : 290 pages
File Size : 46,8 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.

Deep Learning for Natural Language Processing

Stephan Raaijmakers

Author : Stephan Raaijmakers
Publisher : Simon and Schuster
Page : 294 pages
File Size : 52,5 Mb
Release : 2022-12-20
Category : Computers
ISBN : 9781638353997

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Deep Learning for Natural Language Processing by Stephan Raaijmakers Pdf

Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Inside Deep Learning for Natural Language Processing you’ll find a wealth of NLP insights, including: An overview of NLP and deep learning One-hot text representations Word embeddings Models for textual similarity Sequential NLP Semantic role labeling Deep memory-based NLP Linguistic structure Hyperparameters for deep NLP Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. About the technology Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. About the book Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You’ll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses! What's inside Improve question answering with sequential NLP Boost performance with linguistic multitask learning Accurately interpret linguistic structure Master multiple word embedding techniques About the reader For readers with intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required. About the author Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO). Table of Contents PART 1 INTRODUCTION 1 Deep learning for NLP 2 Deep learning and language: The basics 3 Text embeddings PART 2 DEEP NLP 4 Textual similarity 5 Sequential NLP 6 Episodic memory for NLP PART 3 ADVANCED TOPICS 7 Attention 8 Multitask learning 9 Transformers 10 Applications of Transformers: Hands-on with BERT

Natural Language Processing with PyTorch

Delip Rao,Brian McMahan

Author : Delip Rao,Brian McMahan
Publisher : O'Reilly Media
Page : 256 pages
File Size : 52,9 Mb
Release : 2019-01-22
Category : Computers
ISBN : 9781491978207

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Natural Language Processing with PyTorch by Delip Rao,Brian McMahan Pdf

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Deep Learning And Linguistic Representation

Deep Learning and Linguistic Representation by Shalom Lappin Pdf

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

Representation Learning for Natural Language Processing by Zhiyuan Liu,Yankai Lin,Maosong Sun Pdf

Deep Learning and Linguistic Representation by Shalom Lappin Pdf

Deep Learning Approaches to Text Production by Shashi Narayan,Claire Gardent Pdf

Deep Learning in Natural Language Processing by Li Deng,Yang Liu Pdf

Embeddings in Natural Language Processing by Mohammad Taher Pilehvar,Jose Camacho-Collados Pdf

Modelling with Words by Jonathan Lawry,Jimi Shanahan,Anca Ralescu Pdf

Representation Learning for Natural Language Processing by Zhiyuan Liu,Yankai Lin,Maosong Sun Pdf

Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision by L. Ashok Kumar,D. Karthika Renuka Pdf

Embeddings in Natural Language Processing by Mohammad Taher Pilehvar,Jose Camacho-Collados Pdf

Neural Network Methods for Natural Language Processing by Yoav Goldberg Pdf

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

Deep Learning for Natural Language Processing by Stephan Raaijmakers Pdf

Natural Language Processing with PyTorch by Delip Rao,Brian McMahan Pdf

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