Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Continuations And Natural Language PDF full book. Access full book title Continuations And Natural Language.
| Author | : Chris Barker |
| Publisher | : OUP Oxford |
| Total Pages | : 251 |
| Release | : 2014 |
| Genre | : Computers |
| ISBN | : 0199575010 |
The continuation of an expression is a portion of its surrounding context. This book proposes and defends the continuation hypothesis: that the meaning of a natural language expression can depend on its own continuation (it can denote a function on its surrounding context).
| Author | : Chris Barker |
| Publisher | : |
| Total Pages | : 228 |
| Release | : 2014 |
| Genre | : Computational linguisitcs |
| ISBN | : 9780191757419 |
The continuation of an expression is a portion of its surrounding context. This book proposes and defends the continuation hypothesis: that the meaning of a natural language expression can depend on its own continuation (it can denote a function on its surrounding context).
| Author | : Hannes Hapke |
| Publisher | : Simon and Schuster |
| Total Pages | : 798 |
| Release | : 2019-03-16 |
| Genre | : Computers |
| ISBN | : 1638356890 |
Summary Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. About the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. What's inside Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines About the Reader This book requires a basic understanding of deep learning and intermediate Python skills. About the Author Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Table of Contents PART 1 - WORDY MACHINES Packets of thought (NLP overview) Build your vocabulary (word tokenization) Math with words (TF-IDF vectors) Finding meaning in word counts (semantic analysis) PART 2 - DEEPER LEARNING (NEURAL NETWORKS) Baby steps with neural networks (perceptrons and backpropagation) Reasoning with word vectors (Word2vec) Getting words in order with convolutional neural networks (CNNs) Loopy (recurrent) neural networks (RNNs) Improving retention with long short-term memory networks Sequence-to-sequence models and attention PART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES) Information extraction (named entity extraction and question answering) Getting chatty (dialog engines) Scaling up (optimization, parallelization, and batch processing)
| Author | : Jacob Eisenstein |
| Publisher | : MIT Press |
| Total Pages | : 536 |
| Release | : 2019-10-01 |
| Genre | : Computers |
| ISBN | : 0262354578 |
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 | : Bhargav Srinivasa-Desikan |
| Publisher | : |
| Total Pages | : 306 |
| Release | : 2018-06-29 |
| Genre | : Computers |
| ISBN | : 9781788838535 |
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
| Author | : Oswald Campesato |
| Publisher | : Mercury Learning and Information |
| Total Pages | : 364 |
| Release | : 2021-06-29 |
| Genre | : Computers |
| ISBN | : 9781683926573 |
This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA ("state of the art"). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included. FEATURES: Covers extensive topics related to natural language processing Includes separate appendices on regular expressions and probability/statistics Features companion files with source code and figures from the book.
| Author | : Steven Bird |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 506 |
| Release | : 2009-06-12 |
| Genre | : Computers |
| ISBN | : 0596555717 |
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 | : Lewis Tunstall |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 429 |
| Release | : 2022-01-26 |
| Genre | : Computers |
| ISBN | : 109810319X |
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
| Author | : David Leithauser |
| Publisher | : |
| Total Pages | : 372 |
| Release | : 1988 |
| Genre | : Artificial intelligence |
| ISBN | : |
| Author | : G.A. Kempen |
| Publisher | : Springer |
| Total Pages | : 466 |
| Release | : 1987-06-30 |
| Genre | : Computers |
| ISBN | : 9024735580 |
Authors and Participants xi I Pragmatic Aspects 1 1. Some pragmatic decision criteria in generation 3 EduardH. Hovy 2. How to appear to be conforming to the 'maxims' even if you prefer to violate them 19 Antlwny Jameson 43 3. Contextual effects on responses to misconceptions Kathleen F. McCoy 4. Generating understandable explanatory sentences 55 Domenico Parisi & Donatella Ferrante 5. Toward a plan-based theory of referring actions 63 Douglas E. Appelt Generating referring expressions and pointing gestures 71 6. Norben Reithinger II Generation of Connected Discourse 83 7. Rhetorical Structure Theory: description and construction of text structures 85 William C. Mann & Sandra A. Tlwmpson 8. Discourse strategies for describing complex physical objects 97 Cecile L. Paris & Kathleen R. McKeown 9. Strategies for generating coherent descriptions of object movements in street scenes 117 Hans-Joachim Novak 133 10. The automated news agency: SEMTEX - a text generator for German Dietmar ROsner 149 11. A connectionist approach to the generation of abstracts KOiti Hasida, Shun Ishizald & Hitoshi Isahara III Generator Design 157 159 12. Factors contributing to efficiency in natural language generation DavidD. McDonald, Marie M. Vaughan & James D. Pustejovsky 183 13. Reviewing as a component of the text generation process Masoud Yazdani A French and English syntactic component for generation 191 14. Laurence Danlos KING: a knowledge-intensive natural language generator 219 15. Paul S. Jacobs vii 231 IV Grammars and Grammatical Formalisms 233 16. The relevance of Tree Adjoining Grammar to generation Aravind K.
