A Foundation for General-purpose Natural Language Generation PDF Download
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Author: Irene Langkilde-Geary
Publisher:
ISBN:
Category :
Languages : en
Pages : 318
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Book Description
Author: Irene Langkilde-Geary
Publisher:
ISBN:
Category :
Languages : en
Pages : 318
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Book Description
Author: Ankur A Patel
Publisher:
ISBN: 9781492062578
Category :
Languages : en
Pages : 350
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Book Description
NLP is one of the hottest topics in AI today. Having lagged for years behind other deep learning fields such as computer vision, NLP only recently gained mainstream popularity. Google, Facebook, and OpenAI have open-sourced large pretrained language models, but many organizations today still struggle with building and adopting NLP applications. This hands-on guide helps you learn the process quickly. If you have a basic to intermediate understanding of machine learning and programming experience with Python, you'll learn how to build and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai walk you through the process without bogging you down in theory. Understand how state-of-the-art NLP models work Learn the tools of the trade, including frameworks popular today Perform NLP tasks such as text classification, semantic search, and reading comprehension Solve problems using new models like transformers and techniques such as transfer learning Build NLP models from scratch with performance comparable or superior to out-of-the-box systems Deploy your models to production and maintain their performance Implement a suite of NLP algorithms using Python and PyTorch
Author: Jacob Eisenstein
Publisher: MIT Press
ISBN: 0262042843
Category : Computers
Languages : en
Pages : 535
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Book Description
A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
Author: Ehud Reiter
Publisher: Cambridge University Press
ISBN: 0521620368
Category : Computers
Languages : en
Pages : 274
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Book Description
This book explains how to build Natural Language Generation (NLG) systems - computer software systems which use techniques from artificial intelligence and computational linguistics to automatically generate understandable texts in English or other human languages, either in isolation or as part of multimedia documents, Web pages, and speech output systems. Typically starting from some non-linguistic representation of information as input, NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It also discusses engineering issues such as system architecture, requirements analysis, and the integration of text generation into multimedia and speech output systems.
Author: Zhiyuan Liu
Publisher: Springer Nature
ISBN: 9811555737
Category : Computers
Languages : en
Pages : 319
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Book Description
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: Gerhard Paaß
Publisher: Springer Nature
ISBN: 3031231902
Category : Computers
Languages : en
Pages : 448
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Book Description
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
Author: Ruth M. Kempson
Publisher: Elsevier
ISBN: 0444517472
Category : Computers
Languages : en
Pages : 596
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Book Description
'Philosophy of linguistics' investigates the foundational concepts and methods of linguistics, the scientific study of human language. It brings together philosophers, scientists and historians to map out both the basic assumptions set during the second half of the last century and the unfolding shifts in perspective in which more functionalist perspectives are explored.
Author: Michael Walker
Publisher:
ISBN: 9781724811745
Category :
Languages : en
Pages : 76
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Book Description
***BUY NOW (Will soon return to 19.59) ******Free eBook for customers who purchase the print book from Amazon*** Are you thinking of learning more about Natural Language Processing (NLP)? This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using ]NLP. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of Data Science and NLP. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction to Natural Language Processing What is Natural Language Processing Perspectivizing NLP: Areas of AI and Their Interdependencies Purpose of Natural Language Processing Text Manipulation Tokenization Stemming Lemmatization Normalization Accessing Text Corpora and Lexical Resources Processing Raw Text Categorizing and Tagging Words NLP Applications Text Classification Sentiment Classification Topic Modelling Question Answering Speech Recognition Machine Translation Word Representation Bag of Words One-Hot Encoding Word Vectors Representation Word2Vec and GloVe Learning to Classify Text Supervised Classification Decision Trees Naive Bayes Classifiers Maximum Entropy Classifiers Deep Learning for NLP What is Deep Learning Feed Forward Neural Networks Recurrent Neural Networks Gated Recurrent Unit Long Short Term Memory Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash NLP concepts and Fundamentals for Beginners from scratch, this book is for you. No need for any coding experience. Q: Does this book include everything I need to become a NLP expert? A: Unfortunately, no. This book is designed for readers taking their first steps in NLP and further learning will be required beyond this book to master all aspects of NLP. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at
[email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/
Author: Steven Bird
Publisher: "O'Reilly Media, Inc."
ISBN: 0596555717
Category : Computers
Languages : en
Pages : 506
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Book Description
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
Author: James G. Shanahan
Publisher: Springer Science & Business Media
ISBN: 1402041020
Category : Computers
Languages : en
Pages : 346
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Book Description
Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the “factual” aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion. Various conceptual models and computational methods are presented. The models explored in this book include the following: distinguishing attitudes from simple factual assertions; distinguishing between the author’s reports from reports of other people’s opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, including indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups about consumer products, political issues, etc. ; brand and reputation management; discovering and predicting consumer and voting trends; analyzing client discourse in therapy and counseling; determining relations between scientific texts by finding reasons for citations; generating more appropriate texts and making agents more believable; and creating writers’ aids. The studies reported here are carried out on different languages such as English, French, Japanese, and Portuguese. Difficult challenges remain, however. It can be argued that analyzing attitude and affect in text is an “NLP”-complete problem.
