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.

The relationship between language and psychology is one that has been studied for centuries. Influencing one another, these two fields uncover how the human mind's processes are interrelated. Psycholinguistics and Cognition in Language Processing is a critical scholarly resource that examines the mystery of language and the obscurity of psychology using innovative studies. Featuring coverage on a broad range of topics, such as language acquisition, emotional aspects in foreign language learning, and speech learning model, this book is geared towards linguists, academicians, practitioners, and researchers, seeking current research on the cognitive and emotional synthetisation of multilingualism.

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

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

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.

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.

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.

Remarkable progress is being made in spoken language processing, but many powerful techniques have remained hidden in conference proceedings and academic papers, inaccessible to most practitioners. In this book, the leaders of the Speech Technology Group at Microsoft Research share these advances -- presenting not just the latest theory, but practical techniques for building commercially viable products.KEY TOPICS: Spoken Language Processing draws upon the latest advances and techniques from multiple fields: acoustics, phonology, phonetics, linguistics, semantics, pragmatics, computer science, electrical engineering, mathematics, syntax, psychology, and beyond. The book begins by presenting essential background on speech production and perception, probability and information theory, and pattern recognition. The authors demonstrate how to extract useful information from the speech signal; then present a variety of contemporary speech recognition techniques, including hidden Markov models, acoustic and language modeling, and techniques for improving resistance to environmental noise. Coverage includes decoders, search algorithms, large vocabulary speech recognition techniques, text-to-speech, spoken language dialog management, user interfaces, and interaction with non-speech interface modalities. The authors also present detailed case studies based on Microsoft's advanced prototypes, including the Whisper speech recognizer, Whistler text-to-speech system, and MiPad handheld computer.MARKET: For anyone involved with planning, designing, building, or purchasing spoken language technology.