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Language and computers

Language and computers

자료유형
단행본
개인저자
Dickinson, Markus. Brew, Chris. Meurers, Detmar.
서명 / 저자사항
Language and computers / Markus Dickinson, Chris Brew, Detmar Meurers.
발행사항
Hoboken :   John Wiley & Sons,   2013.  
형태사항
232 p. : ill. ; 26 cm.
ISBN
9781405183062 (cloth) 9781405183055 (pbk.)
서지주기
Includes bibliographical references and index.
일반주제명
Computational linguistics. Natural language processing (Computer science)
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001 000045732554
005 20121220173821
008 121220s2013 njua b 001 0 eng
010 ▼a 2012010324
020 ▼a 9781405183062 (cloth)
020 ▼a 9781405183055 (pbk.)
035 ▼a (KERIS)REF000016757013
040 ▼a DLC ▼c DLC ▼d 211009
050 0 0 ▼a P98 ▼b .D495 2013
082 0 0 ▼a 410.285 ▼2 23
084 ▼a 410.285 ▼2 DDCK
090 ▼a 410.285 ▼b D553L
100 1 ▼a Dickinson, Markus.
245 1 0 ▼a Language and computers / ▼c Markus Dickinson, Chris Brew, Detmar Meurers.
260 ▼a Hoboken : ▼b John Wiley & Sons, ▼c 2013.
300 ▼a 232 p. : ▼b ill. ; ▼c 26 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Computational linguistics.
650 0 ▼a Natural language processing (Computer science)
700 1 ▼a Brew, Chris.
700 1 ▼a Meurers, Detmar.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.285 D553L 등록번호 111682949 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

저자소개

Markus Dickinson(지은이)

미국 인디애나 대학교 언어학과 조교수

Chris Brew(지은이)

미국 교육 시험 서비스 (ETS) 선임 연구위원

Detmar Meurers(지은이)

독일 튀빙엔 대학교 전산언어학 교수

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목차

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


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