An introduction to language processing with Perl and Prolog: an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2006
|
| Schriftenreihe: | Cognitive technologies
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 2. ed. u.d.T.: Nugues, Pierre M.: Language processing with Perl and Prolog Literaturverz. S. 497 - 513 |
| Beschreibung: | XX, 513 S. graph. Darst. 21 cm |
| ISBN: | 9783540250319 354025031X |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV021591003 | ||
| 003 | DE-604 | ||
| 005 | 20150618 | ||
| 007 | t | ||
| 008 | 060522s2006 gw d||| |||| 00||| eng d | ||
| 015 | |a 05,N35,0059 |2 dnb | ||
| 015 | |a 06,A21,0078 |2 dnb | ||
| 016 | 7 | |a 975942018 |2 DE-101 | |
| 020 | |a 9783540250319 |c Pp. : EUR 90.90 (freier Pr.), ca. sfr 152.50 (freier Pr.) |9 978-3-540-25031-9 | ||
| 020 | |a 354025031X |c Pp. : EUR 90.90 (freier Pr.), ca. sfr 152.50 (freier Pr.) |9 3-540-25031-X | ||
| 024 | 3 | |a 9783540250319 | |
| 028 | 5 | 2 | |a 11398141 |
| 035 | |a (OCoLC)181471510 | ||
| 035 | |a (DE-599)BVBBV021591003 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-BE | ||
| 049 | |a DE-19 |a DE-473 |a DE-29T |a DE-29 |a DE-20 | ||
| 084 | |a ST 250 |0 (DE-625)143626: |2 rvk | ||
| 084 | |a ST 278 |0 (DE-625)143644: |2 rvk | ||
| 084 | |a ST 306 |0 (DE-625)143654: |2 rvk | ||
| 084 | |a 004 |2 sdnb | ||
| 084 | |a 400 |2 sdnb | ||
| 100 | 1 | |a Nugues, Pierre M. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a An introduction to language processing with Perl and Prolog |b an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables |c Pierre M. Nugues |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2006 | |
| 300 | |a XX, 513 S. |b graph. Darst. |c 21 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Cognitive technologies | |
| 500 | |a 2. ed. u.d.T.: Nugues, Pierre M.: Language processing with Perl and Prolog | ||
| 500 | |a Literaturverz. S. 497 - 513 | ||
| 650 | 4 | |a Natural language processing (Computer science) | |
| 650 | 4 | |a Perl (Computer program language) | |
| 650 | 4 | |a Prolog (Computer program language) | |
| 650 | 0 | 7 | |a Natürliche Sprache |0 (DE-588)4041354-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a PROLOG |g Programmiersprache |0 (DE-588)4047464-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Perl |g Programmiersprache |0 (DE-588)4307836-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computerlinguistik |0 (DE-588)4035843-4 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Natürliche Sprache |0 (DE-588)4041354-8 |D s |
| 689 | 0 | 1 | |a Computerlinguistik |0 (DE-588)4035843-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Natürliche Sprache |0 (DE-588)4041354-8 |D s |
| 689 | 1 | 1 | |a Computerlinguistik |0 (DE-588)4035843-4 |D s |
| 689 | 1 | 2 | |a PROLOG |g Programmiersprache |0 (DE-588)4047464-1 |D s |
| 689 | 1 | |5 DE-604 | |
| 689 | 2 | 0 | |a Natürliche Sprache |0 (DE-588)4041354-8 |D s |
| 689 | 2 | 1 | |a Computerlinguistik |0 (DE-588)4035843-4 |D s |
| 689 | 2 | 2 | |a Perl |g Programmiersprache |0 (DE-588)4307836-9 |D s |
| 689 | 2 | |5 DE-604 | |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806488&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-014806488 | ||
Datensatz im Suchindex
| _version_ | 1804135367693041664 |
|---|---|
| adam_text | PIERR
E M. NUGUES
AN INTRODUCTION TO
LANGUAGE PROCESSING
WITH PERL AND PROLOG
AN OUTLINE OF THEORIES, IMPLEMENTATION
, AN
D APPLICATION
WIT
H SPECIAL CONSIDERATIO
N OF ENGLISH, FRENCH, AN
D GERMA
N
WITH 153 FIGURES AND 192 TABLES
YU.
SPRINGER
CONTENTS
1 AN OVERVIEW OF LANGUAGE PROCESSING
1
1.1 LINGUISTICS AND LANGUAGE PROCESSING 1
1.2 APPLICATIONS OF LANGUAGE PROCESSING 2
1.3 THE DIFFERENT DOMAINS OF LANGUAGE PROCESSING 3
1.4 PHONETICS 4
1.5 LEXICON AND MORPHOLOGY 6
1.6 SYNTAX 8
1.6.1 SYNTAX AS DEFINED BY NOAM CHOMSKY 8
1.6.2 SYNTAX AS RELATIONS AND DEPENDENCIES 10
1.7 SEMANTICS 11
1.8 DISCOURSE AND DIALOGUE 14
1.9 WHY SPEECH AND LANGUAGE PROCESSING ARE DIFFICULT 14
1.9.1 AMBIGUITY 15
1.9.2 MODELS AND THEIR IMPLEMENTATION 16
1.10 AN EXAMPLE OF LANGUAGE TECHNOLOGY IN ACTION: THE PERSONA PROJECT 17
1.10.1 OVERVIEW OF PERSONA 17
1.10.2 THE PERSONA S MODULES 18
1.11 FURTHER READING 19
2 CORPUS PROCESSING TOOLS
23
2.1 CORPORA 23
2.1.1 TYPES OF CORPORA 23
2.1.2 CORPORA AND LEXICON BUILDING 24
2.1.3 CORPORA AS KNOWLEDGE SOURCES FOR THE LINGUIST 26
2.2 FINITE-STATE AUTOMATA 27
2.2.1 A DESCRIPTION 27
2.2.2 MATHEMATICAL DEFINITION OF FINITE-STATE AUTOMATA 28
2.2.3 FINITE-STATE AUTOMATA IN PROLOG 29
2.2.4 DETERMINISTIC AND NONDETERMINISTIC AUTOMATA 30
2.2.5 BUILDING A DETERMINISTIC AUTOMATA FROM A
NONDETERMINISTIC ONE 31
CONTENTS
2.2.6 SEARCHING A STRING WITH A FINITE-STATE AUTOMATON 31
2.2.7 OPERATIONS ON FINITE-STATE AUTOMATA 33
2.3 REGULAR EXPRESSIONS 35
2.3.1 REPETITION METACHARACTERS 36
2.3.2 THE LONGEST MATCH 37
2.3.3 CHARACTER CLASSES 38
2.3.4 NONPRINTABLE SYMBOLS OR POSITIONS 39
2.3.5 UNION AND BOOLEAN OPERATORS 41
2.3.6 OPERATOR COMBINATION AND PRECEDENCE 41
2.4 PROGRAMMING WITH REGULAR EXPRESSIONS 42
2.4.1 PERL 42
2.4.2 MATCHING 42
2.4.3 SUBSTITUTIONS 43
2.4.4 TRANSLATING CHARACTERS 44
2.4.5 STRING OPERATORS 44
2.4.6 BACK REFERENCES 45
2.5 FINDING CONCORDANCES 46
2.5.1 CONCORDANCES IN PROLOG 4
6
2.5.2 CONCORDANCES IN PERL 48
2.6 APPROXIMATE STRING MATCHING 50
2.6.1 EDIT OPERATIONS 50
2.6.2 MINIMUM EDIT DISTANCE 51
2.6.3 SEARCHING EDITS IN PROLOG 54
2.7 FURTHER READING 55
ENCODING, ENTROPY, AND ANNOTATION SCHEMES
59
3.1 ENCODING TEXTS 59
3.2 CHARACTER SETS 60
3.2.1 REPRESENTING CHARACTERS 60
3.2.2 UNICODE 61
3.2.3 THE UNICODE ENCODING SCHEMES 63
3.3 LOCALES AND WORD ORDER 66
3.3.1 PRESENTING TIME, NUMERICAL INFORMATION, AND ORDERED
WORDS 66
3.3.2 THE UNICODE COLLATION ALGORITHM 67
3.4 MARKUP LANGUAGES 69
3.4.1 A BRIEF BACKGROUND 69
3.4.2 AN OUTLINE OF XML 69
3.4.3 WRITING A DTD 71
3.4.4 WRITING AN XML DOCUMENT 74
3.4.5 NAMESPACES 75
3.5 CODES AND INFORMATION THEORY 76
3.5.1 ENTROPY 76
3.5.2 HUFFMAN ENCODING 77
3.5.3 CROSS ENTROPY 80
CONTENTS XI
3.5.4 PERPLEXITY AND CROSS PERPLEXITY 81
3.6 ENTROPY AND DECISION TREES 82
3.6.1 DECISION TREES 82
3.6.2 INDUCING DECISION TREES AUTOMATICALLY 82
3.7 FURTHER READING 84
COUNTING WORDS
87
4.1 COUNTING WORDS AND WORD SEQUENCES 87
4.2 WORDS AND TOKENS 87
4.2.1 WHAT IS A WORD? 87
4.2.2 BREAKING A TEXT INTO WORDS: TOKENIZATION 88
4.3 TOKENIZING TEXTS 89
4.3.1 TOKENIZING TEXTS IN PROLOG 89
4.3.2 TOKENIZING TEXTS IN PERL 91
4.4 TV-GRAMS 92
4.4.1 SOME DEFINITIONS 92
4.4.2 COUNTING UNIGRAMS IN PROLOG 93
4.4.3 COUNTING UNIGRAMS WITH PERL 93
4.4.4 COUNTING BIGRAMS WITH PERL 95
4.5 PROBABILISTIC MODELS OF A WORD SEQUENCE 95
4.5.1 THE MAXIMUM LIKELIHOOD ESTIMATION 95
4.5.2 USING ML ESTIMATES WITH
NINETEEN EIGHTY-FOUR
97
4.6 SMOOTHING TV-GRAM PROBABILITIES 99
4.6.1 SPARSE DATA 99
4.6.2 LAPLACE S RULE 100
4.6.3 GOOD-TURING ESTIMATION 101
4.7 USING TV-GRAMS OF VARIABLE LENGTH 102
4.7.1 LINEAR INTERPOLATION 103
4.7.2 BACK-OFF 104
4.8 QUALITY OF A LANGUAGE MODEL 104
4.8.1 INTUITIVE PRESENTATION 104
4.8.2 ENTROPY RATE 105
4.8.3 CROSS ENTROPY 105
4.8.4 PERPLEXITY 106
4.9 COLLOCATIONS 106
4.9.1 WORD PREFERENCE MEASUREMENTS 107
4.9.2 EXTRACTING COLLOCATIONS WITH PERL 108
4.10 APPLICATION: RETRIEVAL AND RANKING OF DOCUMENTS ON THE WEB ...
. 109
4.11 FURTHER READING IL
L
WORDS, PARTS OF SPEECH, AND MORPHOLOGY
113
5.1 WORDS 113
5.1.1 PARTS OF SPEECH 113
5.1.2 FEATURES 114
5.1.3 TWO SIGNIFICANT PARTS OF SPEECH: THE NOUN AND THE VERB .
. 115
XII CONTENTS
5.2 LEXICONS 117
5.2.1 ENCODING A DICTIONARY 119
5.2.2 BUILDING A TRIE IN PROLOG 121
5.2.3 FINDING A WORD IN A TRIE 123
5.3 MORPHOLOGY 123
5.3.1 MORPHEMES 123
5.3.2 MORPHS 124
5.3.3 INFLECTION AND DERIVATION 125
5.3.4 LANGUAGE DIFFERENCES 129
5.4 MORPHOLOGICAL PARSING 130
5.4.1 TWO-LEVEL MODEL OF MORPHOLOGY 130
5.4.2 INTERPRETING THE MORPHS 131
5.4.3 FINITE-STATE TRANSDUCERS 131
5.4.4 CONJUGATING A FRENCH VERB 133
5.4.5 PROLOG IMPLEMENTATION 134
5.4.6 AMBIGUITY 136
5.4.7 OPERATIONS ON FINITE-STATE TRANSDUCERS 137
5.5 MORPHOLOGICAL RULES 138
5.5.1 TWO-LEVEL RULES 138
5.5.2 RULES AND FINITE-STATE TRANSDUCERS 139
5.5.3 RULE COMPOSITION: AN EXAMPLE WITH FRENCH IRREGULAR VERBS 141
5.6 APPLICATION EXAMPLES 142
5.7 FURTHER READING 142
6 PART-OF-SPEECH TAGGING USING RULES
147
6.1 RESOLVING PART-OF-SPEECH AMBIGUITY 147
6.1.1 A MANUAL METHOD 147
6.1.2 WHICH METHOD TO USE TO AUTOMATICALLY ASSIGN PARTS OF
SPEECH 147
6.2 TAGGING WITH RULES 149
6.2.1 BRILL S TAGGER 149
6.2.2 IMPLEMENTATION IN PROLOG 151
6.2.3 DERIVING RULES AUTOMATICALLY 153
6.2.4 CONFUSION MATRICES 154
6.3 UNKNOWN WORDS 154
6.4 STANDARDIZED PART-OF-SPEECH TAGSETS 156
6.4.1 MULTILINGUAL PART-OF-SPEECH TAGS 156
6.4.2 PARTS OF SPEECH FOR ENGLISH 158
6.4.3 AN ANNOTATION SCHEME FOR SWEDISH 160
6.5 FURTHER READING 162
CONTENTS XIII
PART-OF-SPEECH TAGGING USING STOCHASTIC TECHNIQUES
163
7.1 THE NOISY CHANNEL MODEL 163
7.1.1 PRESENTATION 163
7.1.2 THE TV-GRAM APPROXIMATION 164
7.1.3 TAGGING A SENTENCE 165
7.1.4 THE VITERBI ALGORITHM: AN INTUITIVE PRESENTATION 166
7.2 MARKOV MODELS 167
7.2.1 MARKOV CHAINS 167
7.2.2 HIDDEN MARKOV MODELS 169
7.2.3 THREE FUNDAMENTAL ALGORITHMS TO SOLVE PROBLEMS WITH
HMMS 170
7.2.4 THE FORWARD PROCEDURE 171
7.2.5 VITERBI ALGORITHM 173
7.2.6 THE BACKWARD PROCEDURE 174
7.2.7 THE FORWARD-BACKWARD ALGORITHM 175
7.3 TAGGING WITH DECISION TREES 177
7.4 UNKNOWN WORDS 179
7.5 AN APPLICATION OF THE NOISY CHANNEL MODEL: SPELL CHECKING 179
7.6 A SECOND APPLICATION: LANGUAGE MODELS FOR MACHINE TRANSLATION . 180
7.6.1 PARALLEL CORPORA 180
7.6.2 ALIGNMENT 181
7.6.3 TRANSLATION 183
7.7 FURTHER READING 184
PHRASE-STRUCTURE GRAMMARS IN PROLOG
185
8.1 USING PROLOG TO WRITE PHRASE-STRUCTURE GRAMMARS 185
8.2 REPRESENTING CHOMSKY S SYNTACTIC FORMALISM IN PROLOG 185
8.2.1 CONSTITUENTS 185
8.2.2 TREE STRUCTURES 186
8.2.3 PHRASE-STRUCTURE RULES 187
8.2.4 THE DEFINITE CLAUSE GRAMMAR (DCG) NOTATION 188
8.3 PARSING WITH DCGS 190
8.3.1 TRANSLATING DCGS INTO PROLOG CLAUSES 190
8.3.2 PARSING AND GENERATION 192
8.3.3 LEFT-RECURSIVE RULES 193
8.4 PARSING AMBIGUITY 194
8.5 USING VARIABLES 196
8.5.1 GENDER AND NUMBER AGREEMENT 196
8.5.2 OBTAINING THE SYNTACTIC STRUCTURE 198
8.6 APPLICATION: TOKENIZING TEXTS USING DCG RULES 200
8.6.1 WORD BREAKING 200
8.6.2 RECOGNITION OF SENTENCE BOUNDARIES 201
8.7 SEMANTIC REPRESENTATION 202
8.7.1 A-CALCULUS 202
8.7.2 EMBEDDING A-EXPRESSIONS INTO DCG RULES 203
XIV CONTENTS
8.7.3 SEMANTIC COMPOSITION OF VERBS 205
8.8 AN APPLICATION OF PHRASE-STRUCTURE GRAMMARS AND A WORKED
EXAMPLE 206
8.9 FURTHER READING 210
9 PARTIAL PARSING
213
9.1 IS SYNTAX NECESSARY? 213
9.2 WORD SPOTTING AND TEMPLATE MATCHING 213
9.2.1 ELIZA 213
9.2.2/ WORD SPOTTING IN PROLOG 214
9.3 MULTIWORD DETECTION 217
9.3.1 MULTIWORDS 217
9.3.2 A STANDARD MULTIWORD ANNOTATION 217
9.3.3 DETECTING MULTIWORDS WITH RULES 219
9.3.4 THE LONGEST MATCH 219
9.3.5 RUNNING THE PROGRAM 220
9.4 NOUN GROUPS AND VERB GROUPS 222
9.4.1 GROUPS VERSUS RECURSIVE PHRASES 223
9.4.2 DCG RULES TO DETECT NOUN GROUPS 223
9.4.3 DCG RULES TO DETECT VERB GROUPS 225
9.4.4 RUNNING THE RULES 226
9.5 GROUP DETECTION AS A TAGGING PROBLEM 227
9.5.1 TAGGING GAPS 227
9.5.2 TAGGING WORDS 228
9.5.3 USING SYMBOLIC RULES 229
9.5.4 USING STATISTICAL TAGGING 229
9.6 CASCADING PARTIAL PARSERS 230
9.7 ELEMENTARY ANALYSIS OF GRAMMATICAL FUNCTIONS 231
9.7.1 MAIN FUNCTIONS 231
9.7.2 EXTRACTING OTHER GROUPS 232
9.8 AN ANNOTATION SCHEME FOR GROUPS IN FRENCH 235
9.9 APPLICATION: THE FASTUS SYSTEM 237
9.9.1 THE MESSAGE UNDERSTANDING CONFERENCES 237
9.9.2 THE SYNTACTIC LAYERS OF THE FASTUS SYSTEM 238
9.9.3 EVALUATION OF INFORMATION EXTRACTION SYSTEMS 239
9.10 FURTHER READING 240
10 SYNTACTIC FORMALISMS
243
10.1 INTRODUCTION 243
10.2 CHOMSKY S GRAMMAR IN SYNTACTIC STRUCTURES 244
10.2.1 CONSTITUENCY: A FORMAL DEFINITION 244
10.2.2 TRANSFORMATIONS 246
10.2.3 TRANSFORMATIONS AND MOVEMENTS 248
10.2.4 GAP THREADING 248
10.2.5 GAP THREADING TO PARSE RELATIVE CLAUSES 250
CONTENTS XV
10.3 STANDARDIZED PHRASE CATEGORIES FOR ENGLISH 252
10.4 UNIFICATION-BASED GRAMMARS 254
10.4.1 FEATURES 254
10.4.2 REPRESENTING FEATURES IN PROLOG 255
10.4.3 A FORMALISM FOR FEATURES AND RULES 257
10.4.4 FEATURES ORGANIZATION 258
10.4.5 FEATURES AND UNIFICATION 260
10.4.6 A UNIFICATION ALGORITHM FOR FEATURE STRUCTURES 261
10.5 DEPENDENCY GRAMMARS 263
10.5.1 PRESENTATION 263
10.5.2 PROPERTIES OF A DEPENDENCY GRAPH 266
10.5.3 VALENCE 268
10.5.4 DEPENDENCIES AND FUNCTIONS 270
10.6 FURTHER READING 273
11 PARSING TECHNIQUES
277
11.1 INTRODUCTION 277
11.2 BOTTOM-UP PARSING 278
11.2.1 THE SHIFT-REDUCE ALGORITHM 278
11.2.2 IMPLEMENTING SHIFT-REDUCE PARSING IN PROLOG 279
11.2.3 DIFFERENCES BETWEEN BOTTOM-UP AND TOP-DOWN PARSING ..
. 281
11.3 CHART PARSING 282
11.3.1 BACKTRACKING AND EFFICIENCY 282
11.3.2 STRUCTURE OF A CHART 282
11.3.3 THE ACTIVE CHART 283
11.3.4 MODULES OF AN EARLEY PARSER 285
11.3.5 THE EARLEY ALGORITHM IN PROLOG 288
11.3.6 THE EARLEY PARSER TO HANDLE LEFT-RECURSIVE RULES AND
EMPTY SYMBOLS 293
11.4 PROBABILISTIC PARSING OF CONTEXT-FREE GRAMMARS 294
11.5 A DESCRIPTION OF PCFGS 294
11.5.1 THE BOTTOM-UP CHART 297
11.5.2 THE COCKE-YOUNGER-KASAMI ALGORITHM IN PROLOG 298
11.5.3 ADDING PROBABILITIES TO THE CYK PARSER 300
11.6 PARSER EVALUATION 301
11.6.1 CONSTITUENCY-BASED EVALUATION 301
11.6.2 DEPENDENCY-BASED EVALUATION 302
11.6.3 PERFORMANCE OF PCFG PARSING 302
11.7 PARSING DEPENDENCIES 303
11.7.1 DEPENDENCY RULES 304
11.7.2 EXTENDING THE SHIFT-REDUCE ALGORITHM TO PARSE
DEPENDENCIES 305
11.7.3 NIVRE S PARSER IN PROLOG 306
11.7.4 FINDING DEPENDENCIES USING CONSTRAINTS 309
11.7.5 PARSING DEPENDENCIES USING STATISTICAL TECHNIQUES 310
XVI CONTENTS
11.8 FURTHER READING 313
12 SEMANTICS AND PREDICATE LOGIC
317
12.1 INTRODUCTION 317
12.2 LANGUAGE MEANING AND LOGIC: AN ILLUSTRATIVE EXAMPLE 317
12.3 FORMAL SEMANTICS 319
12.4 FIRST-ORDER PREDICATE CALCULUS TO REPRESENT THE STATE OF AFFAIRS ..
. 319
12.4.1 VARIABLES AND CONSTANTS 320
12.4.2 PREDICATES 320
12.5 QUEUIN
G THE UNIVERSE OF DISCOURSE 322
12.6 MAPPING PHRASES ONTO LOGICAL FORMULAS 322
12.6.1 REPRESENTING NOUNS AND ADJECTIVES 323
12.6.2 REPRESENTING NOUN GROUPS 324
12.6.3 REPRESENTING VERBS AND PREPOSITIONS 324
12.7 THE CASE OF DETERMINERS 325
12.7.1 DETERMINERS AND LOGIC QUANTIFIERS 325
12.7.2 TRANSLATING SENTENCES USING QUANTIFIERS 326
12.7.3 A GENERAL REPRESENTATION OF SENTENCES 327
12.8 COMPOSITIONALITY TO TRANSLATE PHRASES TO LOGICAL FORMS 329
12.8.1 TRANSLATING THE NOUN PHRASE 329
12.8.2 TRANSLATING THE VERB PHRASE 330
12.9 AUGMENTING THE DATABASE AND ANSWERING QUESTIONS 331
12.9.1 DECLARATIONS 332
12.9.2 QUESTIONS WITH EXISTENTIAL AND UNIVERSAL QUANTIFIERS 332
12.9.3 PROLOG AND UNKNOWN PREDICATES 334
12.9.4 OTHER DETERMINERS AND QUESTIONS 335
12.10 APPLICATION: THE SPOKEN LANGUAGE TRANSLATOR 335
12.10.1 TRANSLATING SPOKEN SENTENCES 335
12.10.2 COMPOSITIONAL SEMANTICS 336
12.10.3 SEMANTIC REPRESENTATION TRANSFER 338
12.11 FURTHER READING 340
13 LEXICAL SEMANTICS
343
1
3.1 BEYOND FORMAL SEMANTICS 343
13.1.1
LA LANGUE ET LA PAROLE
343
13.1.2 LANGUAGE AND THE STRUCTURE OF THE WORLD 343
13.2 LEXICAL STRUCTURES 344
13.2.1 SOME BASIC TERMS AND CONCEPTS 344
13.2.2 ONTOLOGICAL ORGANIZATION 344
13.2.3 LEXICAL CLASSES AND RELATIONS 345
13.2.4 SEMANTIC NETWORKS 347
13.3 BUILDING A LEXICON 347
13.3.1 THE LEXICON AND WORD SENSES 349
13.3.2 VERB MODELS 350
13.3.3 DEFINITIONS 351
CONTENTS XVII
13.4 AN EXAMPLE OF EXHAUSTIVE LEXICAL ORGANIZATION: WORDNET 352
13.4.1 NOUNS 353
13.4.2 ADJECTIVES 354
13.4.3 VERBS 355
13.5 AUTOMATIC WORD SENSE DISAMBIGUATION 356
13.5.1 SENSES AS TAGS 356
13.5.2 ASSOCIATING A WORD WITH A CONTEXT 357
13.5.3 GUESSING THE TOPIC 357
13.5.4 NAIVE BAYES 358
13.5.5 USING CONSTRAINTS ON VERBS 359
13.5.6 USING DICTIONARY DEFINITIONS 359
13.5.7 AN UNSUPERVISED ALGORITHM TO TAG SENSES 360
13.5.8 SENSES AND LANGUAGES 362
13.6 CASE GRAMMARS 363
13.6.1 CASES IN LATIN 363
13.6.2 CASES AND THEMATIC ROLES 364
13.6.3 PARSING WITH CASES 365
13.6.4 SEMANTIC GRAMMARS 366
13.7 EXTENDING CASE GRAMMARS 367
13.7.1 FRAMENET 367
13.7.2 A STATISTICAL METHOD TO IDENTIFY SEMANTIC ROLES 368
13.8 AN EXAMPLE OF CASE GRAMMAR APPLICATION: EVAR 371
13.8.1 EVAR S ONTOLOGY AND SYNTACTIC CLASSES 371
13.8.2 CASES IN EVAR 373
13.9 FURTHER READING 373
14 DISCOURSE
377
14.1 INTRODUCTION 377
14.2 DISCOURSE: A MINIMALIST DEFINITION 378
14.2.1 A DESCRIPTION OF DISCOURSE 378
14.2.2 DISCOURSE ENTITIES 378
14.3 REFERENCES: AN APPLICATION-ORIENTED VIEW 379
14.3.1 REFERENCES AND NOUN PHRASES 379
14.3.2 FINDING NAMES - PROPER NOUNS 380
14.4 COREFERENCE 381
14.4.1 ANAPHORA 381
14.4.2 SOLVING COREFERENCES IN AN EXAMPLE 382
14.4.3 A STANDARD COREFERENCE ANNOTATION 383
14.5 REFERENCES: A MORE FORMAL VIEW 384
14.5.1 GENERATING DISCOURSE ENTITIES: THE EXISTENTIAL QUANTIFIER . 384
14.5.2 RETRIEVING DISCOURSE ENTITIES: DEFINITE DESCRIPTIONS 385
14.5.3 GENERATING DISCOURSE ENTITIES: THE UNIVERSAL QUANTIFIER . . 386
14.6 CENTERING: A THEORY ON DISCOURSE STRUCTURE 387
14.7 SOLVING COREFERENCES 388
XVIII CONTENTS
14.7.1 A SIMPLISTIC METHOD: USING SYNTACTIC AND SEMANTIC
COMPATIBILITY 389
14.7.2 SOLVING COREFERENCES WITH SHALLOW GRAMMATICAL
INFORMATION 390
14.7.3 SALIENCE IN A MULTIMODAL CONTEXT 391
14.7.4 USING A MACHINE-LEARNING TECHNIQUE TO RESOLVE
COREFERENCES 391
14.7.5 MORE COMPLEX PHENOMENA: ELLIPSES 396
14.8 DISCOURSE AND RHETORIC 396
14.8U ANCIENT RHETORIC: AN OUTLINE 397
14.8.2 RHETORICAL STRUCTURE THEORY 397
14.8.3 TYPES OF RELATIONS 399
14.8.4 IMPLEMENTING RHETORICAL STRUCTURE THEORY 400
14.9 EVENTS AND TIME 401
14.9.1 EVENTS 403
14.9.2 EVENT TYPES 404
14.9.3 TEMPORAL REPRESENTATION OF EVENTS 404
14.9.4 EVENTS AND TENSES 406
14.10 TIMEML, AN ANNOTATION SCHEME FOR TIME AND EVENTS 407
14.11 FURTHER READING 409
15 DIALOGUE
411
15.1 INTRODUCTION 411
15.2 WHY A DIALOGUE? 411
15.3 SIMPLE DIALOGUE SYSTEMS 412
15.3.1 DIALOGUE SYSTEMS BASED ON AUTOMATA 412
15.3.2 DIALOGUE MODELING 413
15.4 SPEECH ACTS: A THEORY OF LANGUAGE INTERACTION 414
15.5 SPEECH ACTS AND HUMAN-MACHINE DIALOGUE 417
15.5.1 SPEECH ACTS AS A TAGGING MODEL 417
15.5.2 SPEECH ACTS TAGS USED IN THE SUNDIAL PROJECT 418
15.5.3 DIALOGUE PARSING 419
15.5.4 INTERPRETING SPEECH ACTS 421
15.5.5 EVAR: A DIALOGUE APPLICATION USING SPEECH ACTS 422
15.6 TAKING BELIEFS AND INTENTIONS INTO ACCOUNT 423
15.6.1 REPRESENTING MENTAL STATES 425
15.6.2 THE STRIPS PLANNING ALGORITHM 427
15.6.3 CAUSALITY 429
15.7 FURTHER READING 430
A
AN INTRODUCTION TO PROLOG
433
A. 1 A SHORT BACKGROUND 433
A.2 BASIC FEATURES OF PROLOG 434
A.2.1 FACTS 434
A.2.2 TERMS 435
CONTENTS XIX
A.2.3 QUERIES 437
A.2.4 LOGICAL VARIABLES 437
A.2.5 SHARED VARIABLES 438
A.2.6 DATA TYPES IN PROLOG 439
A.2.7 RULES 440
A.3 RUNNING A PROGRAM 442
A.4 UNIFICATION 443
A.4.1 SUBSTITUTION AND INSTANCES 443
A.4.2 TERMS AND UNIFICATION 444
A.4.3 THE HERBRAND UNIFICATION ALGORITHM 445
A.4.4 EXAMPLE 445
A.4.5 THE OCCURS-CHECK 446
A.5 RESOLUTION 447
A.5.1 MODUS PONENS 447
A.5.2 A RESOLUTION ALGORITHM 447
A.5.3 DERIVATION TREES AND BACKTRACKING 448
A.6 TRACING AND DEBUGGING 450
A.7 CUTS, NEGATION, AND RELATED PREDICATES 452
A.7.1 CUTS 452
A.7.2 NEGATION 453
A.7.3 THE ONCE/
1 PREDICATE 454
A.8 LISTS 455
A.9 SOME LIST-HANDLING PREDICATES 456
A.9.1 THE MEMBER/
2 PREDICATE 456
A.9.2 THE APPEND/
3 PREDICATE 457
A.9.3 THE DELETE/
3 PREDICATE 458
A.9.4 THE INTERSECTION/
3 PREDICATE 458
A.9.5 THE REVERSE/
2 PREDICATE 459
A.9.6 THE MODE OF AN ARGUMENT 459
A. 10 OPERATORS AND ARITHMETIC 460
A.10.1 OPERATORS 460
A.10.2 ARITHMETIC OPERATIONS 460
A. 10.3 COMPARISON OPERATORS 462
A.10.4 LISTS AND ARITHMETIC: THE LENGTH/
2 PREDICATE 463
A.10.5 LISTS AND COMPARISON: THE QUICKSORT/
2 PREDICATE ...
. 463
A. 11 SOME OTHER BUILT-IN PREDICATES 464
A. 11.1 TYPE PREDICATES 464
A. 11.2 TERM MANIPULATION PREDICATES 465
A. 12 HANDLING RUN-TIME ERRORS AND EXCEPTIONS 466
A. 13 DYNAMICALLY ACCESSING AND UPDATING THE DATABASE 467
A.13.1 ACCESSING A CLAUSE: THE CLAUSE/
2 PREDICATE 467
A.13.2 DYNAMIC AND STATIC PREDICATES 468
A.13.3 ADDING A CLAUSE: THE ASSERTA/
1 AND ASSERTZ/
1
PREDICATES 468
XX CONTENTS
A.13.4 REMOVING CLAUSES: THE RETRACT/
1 AND ABOLISH/
2
PREDICATES 469
A.13.5 HANDLING UNKNOWN PREDICATES 470
A.14 ALL-SOLUTIONS PREDICATES 470
A. 15 FUNDAMENTAL SEARCH ALGORITHMS 471
A.15.1 REPRESENTING THE GRAPH 472
A.15.2 DEPTH-FIRST SEARCH 473
A.15.3 BREADTH-FIRST SEARCH 474
A.15.4 A* SEARCH 475
A. 16 INPUT/OUTPUT 476
A. 16.1 READING AND WRITING CHARACTERS WITH EDINBURGH PROLOG .
. . 476
A. 16.2 READING AND WRITING TERMS WITH EDINBURGH PROLOG 476
A. 16.3 OPENING AND CLOSING FILES WITH EDINBURGH PROLOG 477
A. 16.4 READING AND WRITING CHARACTERS WITH STANDARD PROLOG ...
. 478
A. 16.5 READING AND WRITING TERMS WITH STANDARD PROLOG 479
A. 16.6 OPENING AND CLOSING FILES WITH STANDARD PROLOG 479
A. 16.7 WRITING LOOPS 480
A. 17 DEVELOPING PROLOG PROGRAMS 481
A.17.1 PRESENTATION STYLE 481
A. 17.2 IMPROVING PROGRAMS 482
INDEX
487
REFERENCES
497
|
| adam_txt |
PIERR
E M. NUGUES
AN INTRODUCTION TO
LANGUAGE PROCESSING
WITH PERL AND PROLOG
AN OUTLINE OF THEORIES, IMPLEMENTATION
, AN
D APPLICATION
WIT
H SPECIAL CONSIDERATIO
N OF ENGLISH, FRENCH, AN
D GERMA
N
WITH 153 FIGURES AND 192 TABLES
YU.
SPRINGER
CONTENTS
1 AN OVERVIEW OF LANGUAGE PROCESSING
1
1.1 LINGUISTICS AND LANGUAGE PROCESSING 1
1.2 APPLICATIONS OF LANGUAGE PROCESSING 2
1.3 THE DIFFERENT DOMAINS OF LANGUAGE PROCESSING 3
1.4 PHONETICS 4
1.5 LEXICON AND MORPHOLOGY 6
1.6 SYNTAX 8
1.6.1 SYNTAX AS DEFINED BY NOAM CHOMSKY 8
1.6.2 SYNTAX AS RELATIONS AND DEPENDENCIES 10
1.7 SEMANTICS 11
1.8 DISCOURSE AND DIALOGUE 14
1.9 WHY SPEECH AND LANGUAGE PROCESSING ARE DIFFICULT 14
1.9.1 AMBIGUITY 15
1.9.2 MODELS AND THEIR IMPLEMENTATION 16
1.10 AN EXAMPLE OF LANGUAGE TECHNOLOGY IN ACTION: THE PERSONA PROJECT 17
1.10.1 OVERVIEW OF PERSONA 17
1.10.2 THE PERSONA'S MODULES 18
1.11 FURTHER READING 19
2 CORPUS PROCESSING TOOLS
23
2.1 CORPORA 23
2.1.1 TYPES OF CORPORA 23
2.1.2 CORPORA AND LEXICON BUILDING 24
2.1.3 CORPORA AS KNOWLEDGE SOURCES FOR THE LINGUIST 26
2.2 FINITE-STATE AUTOMATA 27
2.2.1 A DESCRIPTION 27
2.2.2 MATHEMATICAL DEFINITION OF FINITE-STATE AUTOMATA 28
2.2.3 FINITE-STATE AUTOMATA IN PROLOG 29
2.2.4 DETERMINISTIC AND NONDETERMINISTIC AUTOMATA 30
2.2.5 BUILDING A DETERMINISTIC AUTOMATA FROM A
NONDETERMINISTIC ONE 31
CONTENTS
2.2.6 SEARCHING A STRING WITH A FINITE-STATE AUTOMATON 31
2.2.7 OPERATIONS ON FINITE-STATE AUTOMATA 33
2.3 REGULAR EXPRESSIONS 35
2.3.1 REPETITION METACHARACTERS 36
2.3.2 THE LONGEST MATCH 37
2.3.3 CHARACTER CLASSES 38
2.3.4 NONPRINTABLE SYMBOLS OR POSITIONS 39
2.3.5 UNION AND BOOLEAN OPERATORS 41
2.3.6 OPERATOR COMBINATION AND PRECEDENCE 41
2.4 PROGRAMMING WITH REGULAR EXPRESSIONS 42
2.4.1 PERL 42
2.4.2 MATCHING 42
2.4.3 SUBSTITUTIONS 43
2.4.4 TRANSLATING CHARACTERS 44
2.4.5 STRING OPERATORS 44
2.4.6 BACK REFERENCES 45
2.5 FINDING CONCORDANCES 46
2.5.1 CONCORDANCES IN PROLOG 4
6
2.5.2 CONCORDANCES IN PERL 48
2.6 APPROXIMATE STRING MATCHING 50
2.6.1 EDIT OPERATIONS 50
2.6.2 MINIMUM EDIT DISTANCE 51
2.6.3 SEARCHING EDITS IN PROLOG 54
2.7 FURTHER READING 55
ENCODING, ENTROPY, AND ANNOTATION SCHEMES
59
3.1 ENCODING TEXTS 59
3.2 CHARACTER SETS 60
3.2.1 REPRESENTING CHARACTERS 60
3.2.2 UNICODE 61
3.2.3 THE UNICODE ENCODING SCHEMES 63
3.3 LOCALES AND WORD ORDER 66
3.3.1 PRESENTING TIME, NUMERICAL INFORMATION, AND ORDERED
WORDS 66
3.3.2 THE UNICODE COLLATION ALGORITHM 67
3.4 MARKUP LANGUAGES 69
3.4.1 A BRIEF BACKGROUND 69
3.4.2 AN OUTLINE OF XML 69
3.4.3 WRITING A DTD 71
3.4.4 WRITING AN XML DOCUMENT 74
3.4.5 NAMESPACES 75
3.5 CODES AND INFORMATION THEORY 76
3.5.1 ENTROPY 76
3.5.2 HUFFMAN ENCODING 77
3.5.3 CROSS ENTROPY 80
CONTENTS XI
3.5.4 PERPLEXITY AND CROSS PERPLEXITY 81
3.6 ENTROPY AND DECISION TREES 82
3.6.1 DECISION TREES 82
3.6.2 INDUCING DECISION TREES AUTOMATICALLY 82
3.7 FURTHER READING 84
COUNTING WORDS
87
4.1 COUNTING WORDS AND WORD SEQUENCES 87
4.2 WORDS AND TOKENS 87
4.2.1 WHAT IS A WORD? 87
4.2.2 BREAKING A TEXT INTO WORDS: TOKENIZATION 88
4.3 TOKENIZING TEXTS 89
4.3.1 TOKENIZING TEXTS IN PROLOG 89
4.3.2 TOKENIZING TEXTS IN PERL 91
4.4 TV-GRAMS 92
4.4.1 SOME DEFINITIONS 92
4.4.2 COUNTING UNIGRAMS IN PROLOG 93
4.4.3 COUNTING UNIGRAMS WITH PERL 93
4.4.4 COUNTING BIGRAMS WITH PERL 95
4.5 PROBABILISTIC MODELS OF A WORD SEQUENCE 95
4.5.1 THE MAXIMUM LIKELIHOOD ESTIMATION 95
4.5.2 USING ML ESTIMATES WITH
NINETEEN EIGHTY-FOUR
97
4.6 SMOOTHING TV-GRAM PROBABILITIES 99
4.6.1 SPARSE DATA 99
4.6.2 LAPLACE'S RULE 100
4.6.3 GOOD-TURING ESTIMATION 101
4.7 USING TV-GRAMS OF VARIABLE LENGTH 102
4.7.1 LINEAR INTERPOLATION 103
4.7.2 BACK-OFF 104
4.8 QUALITY OF A LANGUAGE MODEL 104
4.8.1 INTUITIVE PRESENTATION 104
4.8.2 ENTROPY RATE 105
4.8.3 CROSS ENTROPY 105
4.8.4 PERPLEXITY 106
4.9 COLLOCATIONS 106
4.9.1 WORD PREFERENCE MEASUREMENTS 107
4.9.2 EXTRACTING COLLOCATIONS WITH PERL 108
4.10 APPLICATION: RETRIEVAL AND RANKING OF DOCUMENTS ON THE WEB .
. 109
4.11 FURTHER READING IL
L
WORDS, PARTS OF SPEECH, AND MORPHOLOGY
113
5.1 WORDS 113
5.1.1 PARTS OF SPEECH 113
5.1.2 FEATURES 114
5.1.3 TWO SIGNIFICANT PARTS OF SPEECH: THE NOUN AND THE VERB .
. 115
XII CONTENTS
5.2 LEXICONS 117
5.2.1 ENCODING A DICTIONARY 119
5.2.2 BUILDING A TRIE IN PROLOG 121
5.2.3 FINDING A WORD IN A TRIE 123
5.3 MORPHOLOGY 123
5.3.1 MORPHEMES 123
5.3.2 MORPHS 124
5.3.3 INFLECTION AND DERIVATION 125
5.3.4 LANGUAGE DIFFERENCES 129
5.4 MORPHOLOGICAL PARSING 130
5.4.1 TWO-LEVEL MODEL OF MORPHOLOGY 130
5.4.2 INTERPRETING THE MORPHS 131
5.4.3 FINITE-STATE TRANSDUCERS 131
5.4.4 CONJUGATING A FRENCH VERB 133
5.4.5 PROLOG IMPLEMENTATION 134
5.4.6 AMBIGUITY 136
5.4.7 OPERATIONS ON FINITE-STATE TRANSDUCERS 137
5.5 MORPHOLOGICAL RULES 138
5.5.1 TWO-LEVEL RULES 138
5.5.2 RULES AND FINITE-STATE TRANSDUCERS 139
5.5.3 RULE COMPOSITION: AN EXAMPLE WITH FRENCH IRREGULAR VERBS 141
5.6 APPLICATION EXAMPLES 142
5.7 FURTHER READING 142
6 PART-OF-SPEECH TAGGING USING RULES
147
6.1 RESOLVING PART-OF-SPEECH AMBIGUITY 147
6.1.1 A MANUAL METHOD 147
6.1.2 WHICH METHOD TO USE TO AUTOMATICALLY ASSIGN PARTS OF
SPEECH 147
6.2 TAGGING WITH RULES 149
6.2.1 BRILL'S TAGGER 149
6.2.2 IMPLEMENTATION IN PROLOG 151
6.2.3 DERIVING RULES AUTOMATICALLY 153
6.2.4 CONFUSION MATRICES 154
6.3 UNKNOWN WORDS 154
6.4 STANDARDIZED PART-OF-SPEECH TAGSETS 156
6.4.1 MULTILINGUAL PART-OF-SPEECH TAGS 156
6.4.2 PARTS OF SPEECH FOR ENGLISH 158
6.4.3 AN ANNOTATION SCHEME FOR SWEDISH 160
6.5 FURTHER READING 162
CONTENTS XIII
PART-OF-SPEECH TAGGING USING STOCHASTIC TECHNIQUES
163
7.1 THE NOISY CHANNEL MODEL 163
7.1.1 PRESENTATION 163
7.1.2 THE TV-GRAM APPROXIMATION 164
7.1.3 TAGGING A SENTENCE 165
7.1.4 THE VITERBI ALGORITHM: AN INTUITIVE PRESENTATION 166
7.2 MARKOV MODELS 167
7.2.1 MARKOV CHAINS 167
7.2.2 HIDDEN MARKOV MODELS 169
7.2.3 THREE FUNDAMENTAL ALGORITHMS TO SOLVE PROBLEMS WITH
HMMS 170
7.2.4 THE FORWARD PROCEDURE 171
7.2.5 VITERBI ALGORITHM 173
7.2.6 THE BACKWARD PROCEDURE 174
7.2.7 THE FORWARD-BACKWARD ALGORITHM 175
7.3 TAGGING WITH DECISION TREES 177
7.4 UNKNOWN WORDS 179
7.5 AN APPLICATION OF THE NOISY CHANNEL MODEL: SPELL CHECKING 179
7.6 A SECOND APPLICATION: LANGUAGE MODELS FOR MACHINE TRANSLATION . 180
7.6.1 PARALLEL CORPORA 180
7.6.2 ALIGNMENT 181
7.6.3 TRANSLATION 183
7.7 FURTHER READING 184
PHRASE-STRUCTURE GRAMMARS IN PROLOG
185
8.1 USING PROLOG TO WRITE PHRASE-STRUCTURE GRAMMARS 185
8.2 REPRESENTING CHOMSKY'S SYNTACTIC FORMALISM IN PROLOG 185
8.2.1 CONSTITUENTS 185
8.2.2 TREE STRUCTURES 186
8.2.3 PHRASE-STRUCTURE RULES 187
8.2.4 THE DEFINITE CLAUSE GRAMMAR (DCG) NOTATION 188
8.3 PARSING WITH DCGS 190
8.3.1 TRANSLATING DCGS INTO PROLOG CLAUSES 190
8.3.2 PARSING AND GENERATION 192
8.3.3 LEFT-RECURSIVE RULES 193
8.4 PARSING AMBIGUITY 194
8.5 USING VARIABLES 196
8.5.1 GENDER AND NUMBER AGREEMENT 196
8.5.2 OBTAINING THE SYNTACTIC STRUCTURE 198
8.6 APPLICATION: TOKENIZING TEXTS USING DCG RULES 200
8.6.1 WORD BREAKING 200
8.6.2 RECOGNITION OF SENTENCE BOUNDARIES 201
8.7 SEMANTIC REPRESENTATION 202
8.7.1 A-CALCULUS 202
8.7.2 EMBEDDING A-EXPRESSIONS INTO DCG RULES 203
XIV CONTENTS
8.7.3 SEMANTIC COMPOSITION OF VERBS 205
8.8 AN APPLICATION OF PHRASE-STRUCTURE GRAMMARS AND A WORKED
EXAMPLE 206
8.9 FURTHER READING 210
9 PARTIAL PARSING
213
9.1 IS SYNTAX NECESSARY? 213
9.2 WORD SPOTTING AND TEMPLATE MATCHING 213
9.2.1 ELIZA 213
9.2.2/ WORD SPOTTING IN PROLOG 214
9.3 MULTIWORD DETECTION 217
9.3.1 MULTIWORDS 217
9.3.2 A STANDARD MULTIWORD ANNOTATION 217
9.3.3 DETECTING MULTIWORDS WITH RULES 219
9.3.4 THE LONGEST MATCH 219
9.3.5 RUNNING THE PROGRAM 220
9.4 NOUN GROUPS AND VERB GROUPS 222
9.4.1 GROUPS VERSUS RECURSIVE PHRASES 223
9.4.2 DCG RULES TO DETECT NOUN GROUPS 223
9.4.3 DCG RULES TO DETECT VERB GROUPS 225
9.4.4 RUNNING THE RULES 226
9.5 GROUP DETECTION AS A TAGGING PROBLEM 227
9.5.1 TAGGING GAPS 227
9.5.2 TAGGING WORDS 228
9.5.3 USING SYMBOLIC RULES 229
9.5.4 USING STATISTICAL TAGGING 229
9.6 CASCADING PARTIAL PARSERS 230
9.7 ELEMENTARY ANALYSIS OF GRAMMATICAL FUNCTIONS 231
9.7.1 MAIN FUNCTIONS 231
9.7.2 EXTRACTING OTHER GROUPS 232
9.8 AN ANNOTATION SCHEME FOR GROUPS IN FRENCH 235
9.9 APPLICATION: THE FASTUS SYSTEM 237
9.9.1 THE MESSAGE UNDERSTANDING CONFERENCES 237
9.9.2 THE SYNTACTIC LAYERS OF THE FASTUS SYSTEM 238
9.9.3 EVALUATION OF INFORMATION EXTRACTION SYSTEMS 239
9.10 FURTHER READING 240
10 SYNTACTIC FORMALISMS
243
10.1 INTRODUCTION 243
10.2 CHOMSKY'S GRAMMAR IN SYNTACTIC STRUCTURES 244
10.2.1 CONSTITUENCY: A FORMAL DEFINITION 244
10.2.2 TRANSFORMATIONS 246
10.2.3 TRANSFORMATIONS AND MOVEMENTS 248
10.2.4 GAP THREADING 248
10.2.5 GAP THREADING TO PARSE RELATIVE CLAUSES 250
CONTENTS XV
10.3 STANDARDIZED PHRASE CATEGORIES FOR ENGLISH 252
10.4 UNIFICATION-BASED GRAMMARS 254
10.4.1 FEATURES 254
10.4.2 REPRESENTING FEATURES IN PROLOG 255
10.4.3 A FORMALISM FOR FEATURES AND RULES 257
10.4.4 FEATURES ORGANIZATION 258
10.4.5 FEATURES AND UNIFICATION 260
10.4.6 A UNIFICATION ALGORITHM FOR FEATURE STRUCTURES 261
10.5 DEPENDENCY GRAMMARS 263
10.5.1 PRESENTATION 263
10.5.2 PROPERTIES OF A DEPENDENCY GRAPH 266
10.5.3 VALENCE 268
10.5.4 DEPENDENCIES AND FUNCTIONS 270
10.6 FURTHER READING 273
11 PARSING TECHNIQUES
277
11.1 INTRODUCTION 277
11.2 BOTTOM-UP PARSING 278
11.2.1 THE SHIFT-REDUCE ALGORITHM 278
11.2.2 IMPLEMENTING SHIFT-REDUCE PARSING IN PROLOG 279
11.2.3 DIFFERENCES BETWEEN BOTTOM-UP AND TOP-DOWN PARSING .
. 281
11.3 CHART PARSING 282
11.3.1 BACKTRACKING AND EFFICIENCY 282
11.3.2 STRUCTURE OF A CHART 282
11.3.3 THE ACTIVE CHART 283
11.3.4 MODULES OF AN EARLEY PARSER 285
11.3.5 THE EARLEY ALGORITHM IN PROLOG 288
11.3.6 THE EARLEY PARSER TO HANDLE LEFT-RECURSIVE RULES AND
EMPTY SYMBOLS 293
11.4 PROBABILISTIC PARSING OF CONTEXT-FREE GRAMMARS 294
11.5 A DESCRIPTION OF PCFGS 294
11.5.1 THE BOTTOM-UP CHART 297
11.5.2 THE COCKE-YOUNGER-KASAMI ALGORITHM IN PROLOG 298
11.5.3 ADDING PROBABILITIES TO THE CYK PARSER 300
11.6 PARSER EVALUATION 301
11.6.1 CONSTITUENCY-BASED EVALUATION 301
11.6.2 DEPENDENCY-BASED EVALUATION 302
11.6.3 PERFORMANCE OF PCFG PARSING 302
11.7 PARSING DEPENDENCIES 303
11.7.1 DEPENDENCY RULES 304
11.7.2 EXTENDING THE SHIFT-REDUCE ALGORITHM TO PARSE
DEPENDENCIES 305
11.7.3 NIVRE'S PARSER IN PROLOG 306
11.7.4 FINDING DEPENDENCIES USING CONSTRAINTS 309
11.7.5 PARSING DEPENDENCIES USING STATISTICAL TECHNIQUES 310
XVI CONTENTS
11.8 FURTHER READING 313
12 SEMANTICS AND PREDICATE LOGIC
317
12.1 INTRODUCTION 317
12.2 LANGUAGE MEANING AND LOGIC: AN ILLUSTRATIVE EXAMPLE 317
12.3 FORMAL SEMANTICS 319
12.4 FIRST-ORDER PREDICATE CALCULUS TO REPRESENT THE STATE OF AFFAIRS .
. 319
12.4.1 VARIABLES AND CONSTANTS 320
12.4.2 PREDICATES 320
12.5 QUEUIN
G THE UNIVERSE OF DISCOURSE 322
12.6 MAPPING PHRASES ONTO LOGICAL FORMULAS 322
12.6.1 REPRESENTING NOUNS AND ADJECTIVES 323
12.6.2 REPRESENTING NOUN GROUPS 324
12.6.3 REPRESENTING VERBS AND PREPOSITIONS 324
12.7 THE CASE OF DETERMINERS 325
12.7.1 DETERMINERS AND LOGIC QUANTIFIERS 325
12.7.2 TRANSLATING SENTENCES USING QUANTIFIERS 326
12.7.3 A GENERAL REPRESENTATION OF SENTENCES 327
12.8 COMPOSITIONALITY TO TRANSLATE PHRASES TO LOGICAL FORMS 329
12.8.1 TRANSLATING THE NOUN PHRASE 329
12.8.2 TRANSLATING THE VERB PHRASE 330
12.9 AUGMENTING THE DATABASE AND ANSWERING QUESTIONS 331
12.9.1 DECLARATIONS 332
12.9.2 QUESTIONS WITH EXISTENTIAL AND UNIVERSAL QUANTIFIERS 332
12.9.3 PROLOG AND UNKNOWN PREDICATES 334
12.9.4 OTHER DETERMINERS AND QUESTIONS 335
12.10 APPLICATION: THE SPOKEN LANGUAGE TRANSLATOR 335
12.10.1 TRANSLATING SPOKEN SENTENCES 335
12.10.2 COMPOSITIONAL SEMANTICS 336
12.10.3 SEMANTIC REPRESENTATION TRANSFER 338
12.11 FURTHER READING 340
13 LEXICAL SEMANTICS
343
1
3.1 BEYOND FORMAL SEMANTICS 343
13.1.1
LA LANGUE ET LA PAROLE
343
13.1.2 LANGUAGE AND THE STRUCTURE OF THE WORLD 343
13.2 LEXICAL STRUCTURES 344
13.2.1 SOME BASIC TERMS AND CONCEPTS 344
13.2.2 ONTOLOGICAL ORGANIZATION 344
13.2.3 LEXICAL CLASSES AND RELATIONS 345
13.2.4 SEMANTIC NETWORKS 347
13.3 BUILDING A LEXICON 347
13.3.1 THE LEXICON AND WORD SENSES 349
13.3.2 VERB MODELS 350
13.3.3 DEFINITIONS 351
CONTENTS XVII
13.4 AN EXAMPLE OF EXHAUSTIVE LEXICAL ORGANIZATION: WORDNET 352
13.4.1 NOUNS 353
13.4.2 ADJECTIVES 354
13.4.3 VERBS 355
13.5 AUTOMATIC WORD SENSE DISAMBIGUATION 356
13.5.1 SENSES AS TAGS 356
13.5.2 ASSOCIATING A WORD WITH A CONTEXT 357
13.5.3 GUESSING THE TOPIC 357
13.5.4 NAIVE BAYES 358
13.5.5 USING CONSTRAINTS ON VERBS 359
13.5.6 USING DICTIONARY DEFINITIONS 359
13.5.7 AN UNSUPERVISED ALGORITHM TO TAG SENSES 360
13.5.8 SENSES AND LANGUAGES 362
13.6 CASE GRAMMARS 363
13.6.1 CASES IN LATIN 363
13.6.2 CASES AND THEMATIC ROLES 364
13.6.3 PARSING WITH CASES 365
13.6.4 SEMANTIC GRAMMARS 366
13.7 EXTENDING CASE GRAMMARS 367
13.7.1 FRAMENET 367
13.7.2 A STATISTICAL METHOD TO IDENTIFY SEMANTIC ROLES 368
13.8 AN EXAMPLE OF CASE GRAMMAR APPLICATION: EVAR 371
13.8.1 EVAR'S ONTOLOGY AND SYNTACTIC CLASSES 371
13.8.2 CASES IN EVAR 373
13.9 FURTHER READING 373
14 DISCOURSE
377
14.1 INTRODUCTION 377
14.2 DISCOURSE: A MINIMALIST DEFINITION 378
14.2.1 A DESCRIPTION OF DISCOURSE 378
14.2.2 DISCOURSE ENTITIES 378
14.3 REFERENCES: AN APPLICATION-ORIENTED VIEW 379
14.3.1 REFERENCES AND NOUN PHRASES 379
14.3.2 FINDING NAMES - PROPER NOUNS 380
14.4 COREFERENCE 381
14.4.1 ANAPHORA 381
14.4.2 SOLVING COREFERENCES IN AN EXAMPLE 382
14.4.3 A STANDARD COREFERENCE ANNOTATION 383
14.5 REFERENCES: A MORE FORMAL VIEW 384
14.5.1 GENERATING DISCOURSE ENTITIES: THE EXISTENTIAL QUANTIFIER . 384
14.5.2 RETRIEVING DISCOURSE ENTITIES: DEFINITE DESCRIPTIONS 385
14.5.3 GENERATING DISCOURSE ENTITIES: THE UNIVERSAL QUANTIFIER . . 386
14.6 CENTERING: A THEORY ON DISCOURSE STRUCTURE 387
14.7 SOLVING COREFERENCES 388
XVIII CONTENTS
14.7.1 A SIMPLISTIC METHOD: USING SYNTACTIC AND SEMANTIC
COMPATIBILITY 389
14.7.2 SOLVING COREFERENCES WITH SHALLOW GRAMMATICAL
INFORMATION 390
14.7.3 SALIENCE IN A MULTIMODAL CONTEXT 391
14.7.4 USING A MACHINE-LEARNING TECHNIQUE TO RESOLVE
COREFERENCES 391
14.7.5 MORE COMPLEX PHENOMENA: ELLIPSES 396
14.8 DISCOURSE AND RHETORIC 396
14.8U ANCIENT RHETORIC: AN OUTLINE 397
14.8.2 RHETORICAL STRUCTURE THEORY 397
14.8.3 TYPES OF RELATIONS 399
14.8.4 IMPLEMENTING RHETORICAL STRUCTURE THEORY 400
14.9 EVENTS AND TIME 401
14.9.1 EVENTS 403
14.9.2 EVENT TYPES 404
14.9.3 TEMPORAL REPRESENTATION OF EVENTS 404
14.9.4 EVENTS AND TENSES 406
14.10 TIMEML, AN ANNOTATION SCHEME FOR TIME AND EVENTS 407
14.11 FURTHER READING 409
15 DIALOGUE
411
15.1 INTRODUCTION 411
15.2 WHY A DIALOGUE? 411
15.3 SIMPLE DIALOGUE SYSTEMS 412
15.3.1 DIALOGUE SYSTEMS BASED ON AUTOMATA 412
15.3.2 DIALOGUE MODELING 413
15.4 SPEECH ACTS: A THEORY OF LANGUAGE INTERACTION 414
15.5 SPEECH ACTS AND HUMAN-MACHINE DIALOGUE 417
15.5.1 SPEECH ACTS AS A TAGGING MODEL 417
15.5.2 SPEECH ACTS TAGS USED IN THE SUNDIAL PROJECT 418
15.5.3 DIALOGUE PARSING 419
15.5.4 INTERPRETING SPEECH ACTS 421
15.5.5 EVAR: A DIALOGUE APPLICATION USING SPEECH ACTS 422
15.6 TAKING BELIEFS AND INTENTIONS INTO ACCOUNT 423
15.6.1 REPRESENTING MENTAL STATES 425
15.6.2 THE STRIPS PLANNING ALGORITHM 427
15.6.3 CAUSALITY 429
15.7 FURTHER READING 430
A
AN INTRODUCTION TO PROLOG
433
A. 1 A SHORT BACKGROUND 433
A.2 BASIC FEATURES OF PROLOG 434
A.2.1 FACTS 434
A.2.2 TERMS 435
CONTENTS XIX
A.2.3 QUERIES 437
A.2.4 LOGICAL VARIABLES 437
A.2.5 SHARED VARIABLES 438
A.2.6 DATA TYPES IN PROLOG 439
A.2.7 RULES 440
A.3 RUNNING A PROGRAM 442
A.4 UNIFICATION 443
A.4.1 SUBSTITUTION AND INSTANCES 443
A.4.2 TERMS AND UNIFICATION 444
A.4.3 THE HERBRAND UNIFICATION ALGORITHM 445
A.4.4 EXAMPLE 445
A.4.5 THE OCCURS-CHECK 446
A.5 RESOLUTION 447
A.5.1 MODUS PONENS 447
A.5.2 A RESOLUTION ALGORITHM 447
A.5.3 DERIVATION TREES AND BACKTRACKING 448
A.6 TRACING AND DEBUGGING 450
A.7 CUTS, NEGATION, AND RELATED PREDICATES 452
A.7.1 CUTS 452
A.7.2 NEGATION 453
A.7.3 THE ONCE/
1 PREDICATE 454
A.8 LISTS 455
A.9 SOME LIST-HANDLING PREDICATES 456
A.9.1 THE MEMBER/
2 PREDICATE 456
A.9.2 THE APPEND/
3 PREDICATE 457
A.9.3 THE DELETE/
3 PREDICATE 458
A.9.4 THE INTERSECTION/
3 PREDICATE 458
A.9.5 THE REVERSE/
2 PREDICATE 459
A.9.6 THE MODE OF AN ARGUMENT 459
A. 10 OPERATORS AND ARITHMETIC 460
A.10.1 OPERATORS 460
A.10.2 ARITHMETIC OPERATIONS 460
A. 10.3 COMPARISON OPERATORS 462
A.10.4 LISTS AND ARITHMETIC: THE LENGTH/
2 PREDICATE 463
A.10.5 LISTS AND COMPARISON: THE QUICKSORT/
2 PREDICATE .
. 463
A. 11 SOME OTHER BUILT-IN PREDICATES 464
A. 11.1 TYPE PREDICATES 464
A. 11.2 TERM MANIPULATION PREDICATES 465
A. 12 HANDLING RUN-TIME ERRORS AND EXCEPTIONS 466
A. 13 DYNAMICALLY ACCESSING AND UPDATING THE DATABASE 467
A.13.1 ACCESSING A CLAUSE: THE CLAUSE/
2 PREDICATE 467
A.13.2 DYNAMIC AND STATIC PREDICATES 468
A.13.3 ADDING A CLAUSE: THE ASSERTA/
1 AND ASSERTZ/
1
PREDICATES 468
XX CONTENTS
A.13.4 REMOVING CLAUSES: THE RETRACT/
1 AND ABOLISH/
2
PREDICATES 469
A.13.5 HANDLING UNKNOWN PREDICATES 470
A.14 ALL-SOLUTIONS PREDICATES 470
A. 15 FUNDAMENTAL SEARCH ALGORITHMS 471
A.15.1 REPRESENTING THE GRAPH 472
A.15.2 DEPTH-FIRST SEARCH 473
A.15.3 BREADTH-FIRST SEARCH 474
A.15.4 A* SEARCH 475
A. 16 INPUT/OUTPUT 476
A. 16.1 READING AND WRITING CHARACTERS WITH EDINBURGH PROLOG .
. . 476
A. 16.2 READING AND WRITING TERMS WITH EDINBURGH PROLOG 476
A. 16.3 OPENING AND CLOSING FILES WITH EDINBURGH PROLOG 477
A. 16.4 READING AND WRITING CHARACTERS WITH STANDARD PROLOG .
. 478
A. 16.5 READING AND WRITING TERMS WITH STANDARD PROLOG 479
A. 16.6 OPENING AND CLOSING FILES WITH STANDARD PROLOG 479
A. 16.7 WRITING LOOPS 480
A. 17 DEVELOPING PROLOG PROGRAMS 481
A.17.1 PRESENTATION STYLE 481
A. 17.2 IMPROVING PROGRAMS 482
INDEX
487
REFERENCES
497 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Nugues, Pierre M. |
| author_facet | Nugues, Pierre M. |
| author_role | aut |
| author_sort | Nugues, Pierre M. |
| author_variant | p m n pm pmn |
| building | Verbundindex |
| bvnumber | BV021591003 |
| classification_rvk | ST 250 ST 278 ST 306 |
| ctrlnum | (OCoLC)181471510 (DE-599)BVBBV021591003 |
| discipline | Informatik Sprachwissenschaft |
| discipline_str_mv | Informatik Sprachwissenschaft |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02864nam a2200661 c 4500</leader><controlfield tag="001">BV021591003</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20150618 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">060522s2006 gw d||| |||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">05,N35,0059</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">06,A21,0078</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">975942018</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783540250319</subfield><subfield code="c">Pp. : EUR 90.90 (freier Pr.), ca. sfr 152.50 (freier Pr.)</subfield><subfield code="9">978-3-540-25031-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">354025031X</subfield><subfield code="c">Pp. : EUR 90.90 (freier Pr.), ca. sfr 152.50 (freier Pr.)</subfield><subfield code="9">3-540-25031-X</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9783540250319</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">11398141</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)181471510</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV021591003</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-BE</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-19</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-20</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 250</subfield><subfield code="0">(DE-625)143626:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 278</subfield><subfield code="0">(DE-625)143644:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 306</subfield><subfield code="0">(DE-625)143654:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">004</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">400</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Nugues, Pierre M.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An introduction to language processing with Perl and Prolog</subfield><subfield code="b">an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables</subfield><subfield code="c">Pierre M. Nugues</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin [u.a.]</subfield><subfield code="b">Springer</subfield><subfield code="c">2006</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XX, 513 S.</subfield><subfield code="b">graph. Darst.</subfield><subfield code="c">21 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Cognitive technologies</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">2. ed. u.d.T.: Nugues, Pierre M.: Language processing with Perl and Prolog</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Literaturverz. S. 497 - 513</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Natural language processing (Computer science)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Perl (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prolog (Computer program language)</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Natürliche Sprache</subfield><subfield code="0">(DE-588)4041354-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">PROLOG</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4047464-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Perl</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4307836-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Computerlinguistik</subfield><subfield code="0">(DE-588)4035843-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Natürliche Sprache</subfield><subfield code="0">(DE-588)4041354-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Computerlinguistik</subfield><subfield code="0">(DE-588)4035843-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Natürliche Sprache</subfield><subfield code="0">(DE-588)4041354-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Computerlinguistik</subfield><subfield code="0">(DE-588)4035843-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="2"><subfield code="a">PROLOG</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4047464-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Natürliche Sprache</subfield><subfield code="0">(DE-588)4041354-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2="1"><subfield code="a">Computerlinguistik</subfield><subfield code="0">(DE-588)4035843-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2="2"><subfield code="a">Perl</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4307836-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">DNB Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806488&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-014806488</subfield></datafield></record></collection> |
| id | DE-604.BV021591003 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:44:26Z |
| indexdate | 2024-07-09T20:39:23Z |
| institution | BVB |
| isbn | 9783540250319 354025031X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014806488 |
| oclc_num | 181471510 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-473 DE-BY-UBG DE-29T DE-29 DE-20 |
| owner_facet | DE-19 DE-BY-UBM DE-473 DE-BY-UBG DE-29T DE-29 DE-20 |
| physical | XX, 513 S. graph. Darst. 21 cm |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series2 | Cognitive technologies |
| spelling | Nugues, Pierre M. Verfasser aut An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables Pierre M. Nugues Berlin [u.a.] Springer 2006 XX, 513 S. graph. Darst. 21 cm txt rdacontent n rdamedia nc rdacarrier Cognitive technologies 2. ed. u.d.T.: Nugues, Pierre M.: Language processing with Perl and Prolog Literaturverz. S. 497 - 513 Natural language processing (Computer science) Perl (Computer program language) Prolog (Computer program language) Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf PROLOG Programmiersprache (DE-588)4047464-1 gnd rswk-swf Perl Programmiersprache (DE-588)4307836-9 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Computerlinguistik (DE-588)4035843-4 s DE-604 PROLOG Programmiersprache (DE-588)4047464-1 s Perl Programmiersprache (DE-588)4307836-9 s DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806488&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nugues, Pierre M. An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables Natural language processing (Computer science) Perl (Computer program language) Prolog (Computer program language) Natürliche Sprache (DE-588)4041354-8 gnd PROLOG Programmiersprache (DE-588)4047464-1 gnd Perl Programmiersprache (DE-588)4307836-9 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| subject_GND | (DE-588)4041354-8 (DE-588)4047464-1 (DE-588)4307836-9 (DE-588)4035843-4 |
| title | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables |
| title_auth | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables |
| title_exact_search | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables |
| title_exact_search_txtP | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables |
| title_full | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables Pierre M. Nugues |
| title_fullStr | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables Pierre M. Nugues |
| title_full_unstemmed | An introduction to language processing with Perl and Prolog an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables Pierre M. Nugues |
| title_short | An introduction to language processing with Perl and Prolog |
| title_sort | an introduction to language processing with perl and prolog an outline of theories implementation and application with special consideration of english french and german with 192 tables |
| title_sub | an outline of theories, implementation, and application with special consideration of English, French, and German ; with ... 192 tables |
| topic | Natural language processing (Computer science) Perl (Computer program language) Prolog (Computer program language) Natürliche Sprache (DE-588)4041354-8 gnd PROLOG Programmiersprache (DE-588)4047464-1 gnd Perl Programmiersprache (DE-588)4307836-9 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| topic_facet | Natural language processing (Computer science) Perl (Computer program language) Prolog (Computer program language) Natürliche Sprache PROLOG Programmiersprache Perl Programmiersprache Computerlinguistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806488&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT nuguespierrem anintroductiontolanguageprocessingwithperlandprologanoutlineoftheoriesimplementationandapplicationwithspecialconsiderationofenglishfrenchandgermanwith192tables |