Language and Computers

Language and Computers

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By (author) Markus Dickinson, By (author) Chris Brew, By (author) Detmar Meurers

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  • Publisher: Wiley-Blackwell (an imprint of John Wiley & Sons Ltd)
  • Format: Paperback | 250 pages
  • Dimensions: 170mm x 244mm x 14mm | 399g
  • Publication date: 22 October 2012
  • Publication City/Country: Chicester
  • ISBN 10: 1405183055
  • ISBN 13: 9781405183055
  • Illustrations note: black & white illustrations, black & white line drawings, black & white tables, figures
  • Sales rank: 478,344

Product description

Language and Computers introduces students to thefundamentals of how computers are used to represent, process, andorganize textual and spoken information. Concepts are grounded inreal-world examples familiar to students experiences ofusing language and computers in everyday life. * A real-world introduction to the fundamentals of how computersprocess language, written specifically for the undergraduateaudience, introducing key concepts from computationallinguistics. * Offers a comprehensive explanation of the problems computersface in handling natural language * Covers a broad spectrum of language-related applications andissues, including major computer applications involving naturallanguage and the social and ethical implications of these newdevelopments * The book focuses on real-world examples with which students canidentify, using these to explore the technology and how itworks * Features under-the-hood sections that givegreater detail on selected advanced topics, rendering the bookappropriate for more advanced courses, or for independent study bythe motivated reader.

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Author information

Markus Dickinson is Assistant Professor at the Department ofLinguistics, Indiana University and currently the director of theComputational Linguistics program. His research focuses onimproving linguistic annotation for natural language processingtechnology and automatically analyzing the language of secondlanguage learners. Chris Brew is a Senior Research Scientist with theEducational Testing Service in Princeton, where he is currently thescientific lead for the c-rater project on automated short answergrading. He has been active in Natural Language Processing for over20 years, first in the UK, then as Associate Professor ofLinguistics and Computer Science at The Ohio State University,where he co-directed the Speech and Language TechnologiesLaboratory, as well as the Computational Linguistics Program. Detmar Meurers is Professor of Computational Linguistics andhead of the Theoretical Computational Linguistics group at theUniversity of Tubingen. He has a longstanding commitment toteaching Computational Linguistics and Linguistics in a way thatcombines current technology and research issues with thefundamentals of the field. His research emphasizes the role oflinguistic insight and linguistic models in ComputationalLinguistics. His most recent research adds a focus on theory andapplications related to second language acquisition.

Review quote

a broad introduction is also needed byBachelor s students in linguistics, and by those studying tobecome language consultants, translators, and so on. This bookwould then be the natural choice. (ComputationalLinguistics, 2013) In general, I can certainly recommend Language andComputers as a broad introduction to language technologyaimed at the general audience and at students who are curious aboutmodern language technology. (Mach Translat, 1March 2013)

Back cover copy

The widening use of computers has powerfully influenced the way people communicate, search and store information. For the majority of individuals and situations, the primary vehicle for such information is natural language, and text and speech are crucial encoding formats for the information revolution. This book introduces students to the fundamentals of how computers are used to represent, process, and organize textual and spoken information. It allows students to effectively understand how the computer works and where the problems arise with the involvement of natural language. Self-contained chapters cover the central analytical concepts and provide students with tips on how to effectively integrate this knowledge into their working practice. The authors ground the concepts and analyses covered in the text in real-world examples familiar to students. Drawing on these examples, the authors teach students how to produce evidence-based analyses and arguments about language. The result is a book that teaches students to generate, justify and argue for valid conclusions about the design, capabilities and behavior of natural language systems.

Table of contents

What This Book Is About xi Overview for Instructors xiii Acknowledgments xvii 1 Prologue : Encoding Language on Computers 1 1.1 Where do we start? 1 1.1.1 Encoding language 2 1.2 Writing systems used for human languages 2 1.2.1 Alphabetic systems 3 1.2.2 Syllabic systems 6 1.2.3 Logographic writing systems 8 1.2.4 Systems with unusual realization 11 1.2.5 Relation to language 11 1.3 Encoding written language 12 1.3.1 Storing information on a computer 12 1.3.2 Using bytes to store characters 14 1.4 Encoding spoken language 17 1.4.1 The nature of speech 17 1.4.2 Articulatory properties 18 1.4.3 Acoustic properties 18 1.4.4 Measuring speech 20 Under the Hood 1: Reading a spectrogram 21 1.4.5 Relating written and spoken language 24 Under the Hood 2: Language modeling for automatic speechrecognition 26 2 Writers Aids 33 2.1 Introduction 33 2.2 Kinds of spelling errors 34 2.2.1 Nonword errors 35 2.2.2 Real-word errors 37 2.3 Spell checkers 38 2.3.1 Nonword error detection 39 2.3.2 Isolated-word spelling correction 41 Under the Hood 3: Dynamic programming 44 2.4 Word correction in context 49 2.4.1 What is grammar? 50 Under the Hood 4: Complexity of languages 56 2.4.2 Techniques for correcting words in context 58 Under the Hood 5: Spell checking for web queries 62 2.5 Style checkers 64 3 Language Tutoring Systems 69 3.1 Learning a language 69 3.2 Computer-assisted language learning 71 3.3 Why make CALL tools aware of language? 73 3.4 What is involved in adding linguistic analysis? 76 3.4.1 Tokenization 76 3.4.2 Part-of-speech tagging 78 3.4.3 Beyond words 80 3.5 An example ICALL system: TAGARELA 81 3.6 Modeling the learner 83 4 Searching 91 4.1 Introduction 91 4.2 Searching through structured data 93 4.3 Searching through unstructured data 95 4.3.1 Information need 95 4.3.2 Evaluating search results 96 4.3.3 Example: Searching the web 97 4.3.4 How search engines work 100 Under the Hood 6: A brief tour of HTML 103 4.4 Searching semi-structured data with regular expressions107 4.4.1 Syntax of regular expressions 108 4.4.2 Grep: An example of using regular expressions 110 Under the Hood 7: Finite-state automata 112 4.5 Searching text corpora 115 4.5.1 Why corpora? 116 4.5.2 Annotated language corpora 117 Under the Hood 8: Searching for linguistic patterns on the web118 5 Classifying Documents : From Junk Mail Detection to SentimentClassification 127 5.1 Automatic document classification 127 5.2 How computers learn 129 5.2.1 Supervised learning 130 5.2.2 Unsupervised learning 131 5.3 Features and evidence 131 5.4 Application: Spam filtering 133 5.4.1 Base rates 135 5.4.2 Payoffs 139 5.4.3 Back to documents 139 5.5 Some types of document classifiers 140 5.5.1 The Naive Bayes classifier 140 Under the Hood 9: Naive Bayes 142 5.5.2 The perceptron 145 5.5.3 Which classifier to use 148 5.6 From classification algorithms to context of use 149 6 Dialog Systems 153 6.1 Computers that converse ? 153 6.2 Why dialogs happen 155 6.3 Automating dialog 156 6.3.1 Getting started 156 6.3.2 Establishing a goal 157 6.3.3 Accepting the user s goal 157 6.3.4 The caller plays her role 158 6.3.5 Giving the answer 158 6.3.6 Negotiating the end of the conversation 159 6.4 Conventions and framing expectations 159 6.4.1 Some framing expectations for games and sports 160 6.4.2 The framing expectations for dialogs 160 6.5 Properties of dialog 161 6.5.1 Dialog moves 161 6.5.2 Speech acts 162 6.5.3 Conversational maxims 164 6.6 Dialog systems and their tasks 166 6.7 Eliza 167 Under the Hood 10: How Eliza works 172 6.8 Spoken dialogs 174 6.9 How to evaluate a dialog system 175 6.10 Why is dialog important? 176 7 Machine Translation Systems 181 7.1 Computers that translate ? 181 7.2 Applications of translation 183 7.2.1 Translation needs 183 7.2.2 What is machine translation really for? 184 7.3 Translating Shakespeare 185 7.4 The translation triangle 188 7.5 Translation and meaning 191 7.6 Words and meanings 193 7.6.1 Words and other languages 193 7.6.2 Synonyms and translation equivalents 194 7.7 Word alignment 194 7.8 IBM Model 1 198 Under the Hood 11: The noisy channel model 200 Under the Hood 12: Phrase-based statistical translation 204 7.9 Commercial automatic translation 205 7.9.1 Translating weather reports 205 7.9.2 Translation in the European Union 207 7.9.3 Prospects for translators 208 8 Epilogue : Impact of Language Technology 215 References 221 Concept Index 227