Deep Learning In Natural Language Processing


Deep Learning In Natural Language Processing
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Deep Learning In Natural Language Processing


Deep Learning In Natural Language Processing
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Author : Li Deng
language : en
Publisher: Springer
Release Date : 2018-05-23

Deep Learning In Natural Language Processing written by Li Deng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-23 with Computers categories.


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.



Deep Learning For Natural Language Processing


Deep Learning For Natural Language Processing
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2017-11-21

Deep Learning For Natural Language Processing written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-21 with Computers categories.


Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.



Deep Learning For Natural Language Processing


Deep Learning For Natural Language Processing
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Author : Karthiek Reddy Bokka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-06-11

Deep Learning For Natural Language Processing written by Karthiek Reddy Bokka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-11 with Computers categories.


Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.



Deep Learning For Natural Language Processing


Deep Learning For Natural Language Processing
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Author : Stephan Raaijmakers
language : en
Publisher: Simon and Schuster
Release Date : 2022-11-29

Deep Learning For Natural Language Processing written by Stephan Raaijmakers and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-29 with Computers categories.


Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.



Deep Learning For Natural Language Processing


Deep Learning For Natural Language Processing
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Author : Palash Goyal
language : en
Publisher: Apress
Release Date : 2018-06-26

Deep Learning For Natural Language Processing written by Palash Goyal and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-26 with Computers categories.


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.



Machine Learning And Deep Learning In Natural Language Processing


Machine Learning And Deep Learning In Natural Language Processing
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Author : Anitha S. Pillai
language : en
Publisher: CRC Press
Release Date : 2023-10-18

Machine Learning And Deep Learning In Natural Language Processing written by Anitha S. Pillai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-18 with Computers categories.


Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results. Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field. NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal. This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.



Machine Learning And Deep Learning In Natural Language Processing


Machine Learning And Deep Learning In Natural Language Processing
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Author : Anitha S. Pillai
language : en
Publisher:
Release Date : 2023-11

Machine Learning And Deep Learning In Natural Language Processing written by Anitha S. Pillai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11 with Deep learning (Machine learning) categories.


"Natural Language Processing (NLP) is a subset of AI, linguistics and computer science, and is concerned with generation, recognition and understanding of human language, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then utilize algorithms to extract meaning and produce results. Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the Natural Language Processing field, in particular, about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content - like emotions but also satirical expressions - and applications in the healthcare field. NLP has the potential to be a disruptive technology in the fields of healthcare, but so far little attention has been devoted to that goal. This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson's disease, or help psychotherapists. The book is intended for a wide audience: beginners will find chapters providing a general introduction to the NLP techniques useful, while experienced professionals would appreciate the chapters about advanced management of emotion, empathy, and non-literal content"--



Getting Started With Deep Learning For Natural Language Processing


Getting Started With Deep Learning For Natural Language Processing
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Author : Sunil Patel
language : en
Publisher: BPB Publications
Release Date : 2021-01-13

Getting Started With Deep Learning For Natural Language Processing written by Sunil Patel and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-13 with Computers categories.


Learn how to redesign NLP applications from scratch. KEY FEATURESÊÊ ¥ Get familiar with the basics of any Machine Learning or Deep Learning application. ¥ Understand how does preprocessing work in NLP pipeline. ¥ Use simple PyTorch snippets to create basic building blocks of the network commonly used inÊ NLP.Ê ¥ Learn how to build a complex NLP application. ¥ Get familiar with the advanced embedding technique, Generative network, and Audio signal processing techniques. ÊÊ DESCRIPTIONÊ Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied. This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). This book not only covers the classical concept of text processing but also shares the recent advancements. This book will empower users in designing networks with the least computational and time complexity. This book not only covers basics of Natural Language Processing but also helps in deciphering the logic behind advanced concepts/architecture such as Batch Normalization, Position Embedding, DenseNet, Attention Mechanism, Highway Networks, Transformer models and Siamese Networks. This book also covers recent advancements such as ELMo-BiLM, SkipThought, and Bert. This book also covers practical implementation with step by step explanation of deep learning techniques in Topic Modelling, Text Generation, Named Entity Recognition, Text Summarization, and Language Translation. In addition to this, very advanced and open to research topics such as Generative Adversarial Network and Speech Processing are also covered. WHAT YOU WILL LEARNÊ ¥ Learn how to leveraging GPU for Deep Learning ¥ Learn how to use complex embedding models such as BERT ¥ Get familiar with the common NLP applications. ¥ Learn how to use GANs in NLP ¥ Learn how to process Speech data and implementing it in Speech applications Ê WHO THIS BOOK IS FORÊ This book is a must-read to everyone who wishes to start the career with Machine learning and Deep Learning. This book is also for those who want to use GPU for developing Deep Learning applications. TABLE OF CONTENTSÊÊ 1. Understanding the basics of learning Process 2. Text Processing Techniques 3. Representing Language Mathematically 4. Using RNN for NLP 5. Applying CNN In NLP Tasks 6. Accelerating NLP with Advanced Embeddings 7. Applying Deep Learning to NLP tasks 8. Application of Complex Architectures in NLP 9. Understanding Generative Networks 10. Techniques of Speech Processing 11. The Road Ahead



Neural Networks For Natural Language Processing


Neural Networks For Natural Language Processing
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Author : S., Sumathi
language : en
Publisher: IGI Global
Release Date : 2019-11-29

Neural Networks For Natural Language Processing written by S., Sumathi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.


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.



Natural Language Processing With Pytorch


Natural Language Processing With Pytorch
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Author : Delip Rao
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-01-22

Natural Language Processing With Pytorch written by Delip Rao and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-22 with Computers categories.


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