Knowledge And Learning In Natural Language

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Knowledge And Learning In Natural Language

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Author : Charles D. Yang
language : en
Publisher: OUP Oxford
Release Date : 2002

Knowledge And Learning In Natural Language written by Charles D. Yang and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Language Arts & Disciplines categories.

This book presents a new theory of how children acquire language and discusses its implications for a wide range of topics. It explores the roles of innateness and experience in language acquisition, provides further evidence for the theory of Universal Grammar, and shows how linguistic development in children is a driving force behind language shifts and changes.Charles Yang surveys a wide range of errors in children's language and identifies overlooked patterns. He combines these with work in biological evolution in order to develop a model of language acquisition by which to understand the interaction between children's internal linguistic knowledge and their external linguistic experience. He then presents evidence from his own and others' research in the acquisition of syntax and morphology and data from historical language change to test its validity. The model is the first to make quantitative and cross-linguistic predictions about child language. It may also be deployed as a predictive model of language change which, when the evidence is available, could explain why grammars change in a particular direction at a particular time.Knowledge and Learning in Natural Language is a pioneering work at the centre of current concerns in linguistics and cognitive science. It will interest all those concerned to understand and explain language acquisition, Universal Grammar, and language change.

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.

Deep Learning In Natural Language Processing

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Author : Li Deng
language : en
Publisher:
Release Date : 2018

Deep Learning In Natural Language Processing written by Li Deng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial Intelligence (incl. Robotics) 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.

Knowledge Representation And The Semantics Of Natural Language

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Author : Hermann Helbig
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-19

Knowledge Representation And The Semantics Of Natural Language written by Hermann Helbig and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-19 with Computers categories.

Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the pres- vation of cultural achievements and their transmission from one generation to the other. During the last few decades, the ?ood of digitalized information has been growing tremendously. This tendency will continue with the globali- tion of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical und- standing and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this c- text, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the g- eration of natural language expressions from formal representations. This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.

Transfer Learning For Natural Language Processing

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Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31

Transfer Learning For Natural Language Processing written by Paul Azunre 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 2021-08-31 with Computers categories.

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

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.

Connectionist Statistical And Symbolic Approaches To Learning For Natural Language Processing

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Author : Stefan Wermter
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-03-15

Connectionist Statistical And Symbolic Approaches To Learning For Natural Language Processing written by Stefan Wermter and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-03-15 with Computers categories.

This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.

Computational Linguistics And Intelligent Text Processing

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Author : Alexander Gelbukh
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-02-16

Computational Linguistics And Intelligent Text Processing written by Alexander Gelbukh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-16 with Computers categories.

This book constitutes the refereed proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009, held in Mexico City, Mexico in March 2009. The 44 revised full papers presented together with 4 invited papers were carefully reviewed and selected from numerous submissions. The papers cover all current issues in computational linguistics research and present intelligent text processing applications.

Machine Learning And Knowledge Extraction

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Author : Andreas Holzinger
language : en
Publisher: Springer
Release Date : 2018-08-23

Machine Learning And Knowledge Extraction written by Andreas Holzinger and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-23 with Computers categories.

This book constitutes the refereed proceedings of the IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018, held in Hamburg, Germany, in September 2018. The 25 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers are clustered under the following topical sections: MAKE-Main Track, MAKE-Text, MAKE-Smart Factory, MAKE-Topology, and MAKE Explainable AI.

Natural Language Processing

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Author : Yue Zhang
language : en
Publisher: Cambridge University Press
Release Date : 2021-01-07

Natural Language Processing written by Yue Zhang 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 2021-01-07 with Computers categories.

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.