Markus Dickinson, Chris Brew, Detmar Meurers

Language and Computers

• Broschiertes Buch

Produktbeschreibung

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 book teaches students to generate, justify and argue for valid conclusions about the design, capabilities and behavior of natural language systems.

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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.

Produktdetails

• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 1A405183050
• 1. Auflage
• Seitenzahl: 256
• Erscheinungstermin: 12. Oktober 2012
• Englisch
• Abmessung: 244mm x 170mm x 15mm
• Gewicht: 404g
• ISBN-13: 9781405183055
• ISBN-10: 1405183055
• Artikelnr.: 36265372

Autorenporträt

Markus Dickinson is Assistant Professor at the Department of Linguistics, Indiana University and currently the director of the Computational Linguistics program. His research focuses on improving linguistic annotation for natural language processing technology and automatically analyzing the language of second language learners. Chris Brew is a Senior Research Scientist with the Educational Testing Service in Princeton, where he is currently the scientific lead for the c-rater project on automated short answer grading. He has been active in Natural Language Processing for over 20 years, first in the UK, then as Associate Professor of Linguistics and Computer Science at The Ohio State University, where he co-directed the Speech and Language Technologies Laboratory, as well as the Computational Linguistics Program. Detmar Meurers is Professor of Computational Linguistics and head of the Theoretical Computational Linguistics group at the University of Tübingen. He has a longstanding commitment to teaching Computational Linguistics and Linguistics in a way that combines current technology and research issues with the fundamentals of the field. His research emphasizes the role of linguistic insight and linguistic models in Computational Linguistics. His most recent research adds a focus on theory and applications related to second language acquisition.

Inhaltsangabe

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 speech
recognition 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 expressions 107 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 web 118 5 Classifying Documents
: From Junk Mail Detection to Sentiment Classification 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