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Introduction to Natural Language Processing
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...
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Natural Language Processing for Social Media, Third Edition
In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms that extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. This book will discuss the challenges in analyzing social media texts in contrast with traditional documents. Researc...
English in Computer-Mediated Communication
This book addresses the nature of English use within contexts of computer-mediated communication (CMC). CMC includes technologies through which not only is language transmitted, but cultures are formed, ideologies are shaped, power is contested, and sociolinguistic boundaries are crossed and blurred. The volume therefore examines the English language in particular in CMC – what it looks like, what it accomplishes, and what it means to speakers.
Natural Language Processing for Social Media
In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods i...
We Are Data
"Algorithms are everywhere, organizing the near-limitless data that exists in our world. Drawing on our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us, and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world but also determine who we are and who we can be. Algorithms use our data to assign our gender, race, sexuality, and citizenship status. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities we inhabit in the process. We have little control over who we algorithmically are. Through a series of entertaining and engaging examples, John Cheney-Lippold draws on the social constructions of identity to advance a new understanding of our algorithmic identities. We Are Data will educate and inspire readers who want to wrest back some freedom in our increasingly surveilled and algorithmically constructed world."--Page 4 of cover
Discourse Processing
Discourse Processing here is framed as marking up a text with structural descriptions on several levels, which can serve to support many language-processing or text-mining tasks. We first explore some ways of assigning structure on the document level: the logical document structure as determined by the layout of the text, its genre-specific content structure, and its breakdown into topical segments. Then the focus moves to phenomena of local coherence. We introduce the problem of coreference and look at methods for building chains of coreferring entities in the text. Next, the notion of coherence relation is introduced as the second important factor of local coherence. We study the role of c...
Linguistic Structure Prediction
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Foundations of Statistical Natural Language Processing
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.
The Handbook of Dialectology
The Handbook of Dialectology provides an authoritative, up-to-date and unusually broad account of the study of dialect, in one volume. Each chapter reviews essential research, and offers a critical discussion of the past, present and future development of the area. The volume is based on state-of-the-art research in dialectology around the world, providing the most current work available with an unusually broad scope of topics Provides a practical guide to the many methodological and statistical issues surrounding the collection and analysis of dialect data Offers summaries of dialect variation in the world's most widely spoken and commonly studied languages, including several non-European languages that have traditionally received less attention in general discussions of dialectology Reviews the intellectual development of the field, including its main theoretical schools of thought and research traditions, both academic and applied The editors are well known and highly respected, with a deep knowledge of this vast field of inquiry
