Reading And Language Processing
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Speech and Language Processing
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
Reading and Language Processing
Author: John M. Henderson
language: en
Publisher: Psychology Press
Release Date: 2013-11-19
This volume was designed to identify the current limits of progress in the psychology of reading and language processing in an information processing framework. Leaders in their fields of interest, the chapter authors couple current theoretical analyses with new, formally presented experiments. The research -- cutting-edge and sometimes controversial -- reflects the prevailing analysis that language comprehension results in numerous levels of representation, including surface features, lexical properties, linguistic structures, and idea networks underlying a message as well as the situations to which a message refers. As a group, the chapters highlight the impact that input modality -- auditory or written -- has on comprehension. Finally, the studies also capture the evolution of new topic matter and ongoing debates concerning the competing paradigms, global proposals, and methods that form the foundation of the enterprise. The book presents current accounts of research on word-, sentence-, and text-processing. It will prove informative for experimental psychologists as well as investigators in cognitive science disciplines such as computer science, linguistics, and educational psychology. The book will also be very helpful to graduate students who wish to develop expertise in the psychology of language processes. For them, it collects, in a single volume, readings that are representative of progress concerning many central problems in the field. As such, it is distinct from the numerous collected volumes that concentrate on a single issue. Complete author and subject indexes facilitate effective use of the volume.
Natural Language Processing in Action
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)