Knowledge And Learning In Natural Language PDF Download
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Author | : Charles D. Yang |
Publisher | : Oxford University Press, USA |
Total Pages | : 196 |
Release | : 2002 |
Genre | : Language Arts & Disciplines |
ISBN | : 9780199254156 |
Download Knowledge and Learning in Natural Language Book in PDF, ePub and Kindle
The model is makes 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.
Author | : Zhiyuan Liu |
Publisher | : Springer Nature |
Total Pages | : 319 |
Release | : 2020-07-03 |
Genre | : Computers |
ISBN | : 9811555737 |
Download Representation Learning for Natural Language Processing Book in PDF, ePub and Kindle
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.
Author | : Paul Azunre |
Publisher | : Simon and Schuster |
Total Pages | : 262 |
Release | : 2021-08-31 |
Genre | : Computers |
ISBN | : 163835099X |
Download Transfer Learning for Natural Language Processing Book in PDF, ePub and Kindle
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
Author | : Li Deng |
Publisher | : Springer |
Total Pages | : 338 |
Release | : 2018-05-23 |
Genre | : Computers |
ISBN | : 9811052093 |
Download Deep Learning in Natural Language Processing Book in PDF, ePub and Kindle
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.
Author | : Sowmya Vajjala |
Publisher | : O'Reilly Media |
Total Pages | : 455 |
Release | : 2020-06-17 |
Genre | : Computers |
ISBN | : 149205402X |
Download Practical Natural Language Processing Book in PDF, ePub and Kindle
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
Author | : Hermann Helbig |
Publisher | : Springer Science & Business Media |
Total Pages | : 652 |
Release | : 2005-12-19 |
Genre | : Computers |
ISBN | : 3540299661 |
Download Knowledge Representation and the Semantics of Natural Language Book in PDF, ePub and Kindle
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.
Author | : Karthiek Reddy Bokka |
Publisher | : Packt Publishing Ltd |
Total Pages | : 372 |
Release | : 2019-06-11 |
Genre | : Computers |
ISBN | : 1838553673 |
Download Deep Learning for Natural Language Processing Book in PDF, ePub and Kindle
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.
Author | : Robert Dale |
Publisher | : CRC Press |
Total Pages | : 974 |
Release | : 2000-07-25 |
Genre | : Business & Economics |
ISBN | : 9780824790004 |
Download Handbook of Natural Language Processing Book in PDF, ePub and Kindle
This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.
Author | : Martin C. Golumbic |
Publisher | : Springer Science & Business Media |
Total Pages | : 315 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 1461390524 |
Download Advances in Artificial Intelligence Book in PDF, ePub and Kindle
Research in artificial intelligence, natural language processing and knowledge-based systems has blossomed during the past decade. At national and international symposia as well as in research centers and universities all over the world, these subjects have been the focus of intense debate and study. This is equally true in Israel which has hosted several international forums on these topics. The articles in this book represent a selection of contributions presented at recent AI conferences held in Israel. A theoretical model for a system that learns from its own experience in playing board games is presented in Learning from Experience in Board Games by Ze'ev Ben-Porat and Martin Golumbic. The model enables such a system to enhance and improve its playing capabilities through the use of a learning mechanism which extracts knowledge from actual playing experience. The learning process requires no external guidance or assistance. This model was implemented and tested on a variant of "Chinese Checkers. " The paper shows the feasibility and validity of the proposed model and investigates the parameters that affect its performance traits. The experimental results give evidence of the validity of the model as a powerful learning mechanism. Original and general algorithms for knowledge extraction and pattern matching were designed and tested as part of the prototype computer system. Analysis of the performance characteristics of these algorithms indicates that they can handle large knowledge bases in an efficient manner.
Author | : Stephan Raaijmakers |
Publisher | : Simon and Schuster |
Total Pages | : 294 |
Release | : 2022-11-29 |
Genre | : Computers |
ISBN | : 1617295442 |
Download Deep Learning for Natural Language Processing Book in PDF, ePub and Kindle
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.