An introduction to language processing with Perl and Prolog : an outline of theories, implementation, and application with special consideration of English, French, and German

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Cover image for An introduction to language processing with Perl and Prolog : an outline of theories, implementation, and application with special consideration of English, French, and German

Title:

Personal Author:

Nugues, Pierre M

Publication Information:

Berlin : Springer, 2006

ISBN:

9783540250319

Subject Term:

Natural language processing (Computer science)

Perl (Computer program language)

PROLOG (Computer program language)

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010113506	QA76.9.N38 N83 2006	Open Access Book	Book	Searching... Unknown

The areas of natural language processing and computational linguistics have continued to grow in recent years, driven by the demand to automatically process text and spoken data. With the processing power and techniques now available, research is scaling up from lab prototypes to real-world, proven applications.

This book teaches the principles of natural language processing, first covering linguistics issues such as encoding, entropy, and annotation schemes; defining words, tokens and parts of speech; and morphology. It then details the language-processing functions involved, including part-of-speech tagging using rules and stochastic techniques; using Prolog to write phase-structure grammars; parsing techniques and syntactic formalisms; semantics, predicate logic and lexical semantics; and analysis of discourse, and applications in dialog systems. The key feature of the book is the author's hands-on approach throughout, with extensive exercises, sample code in Prolog and Perl, and a detailed introduction to Prolog. The reader is supported with a companion website that contains teaching slides, programs, and additional material.

The book is suitable for researchers and students of natural language processing and computational linguistics.

Author Notes

Pierre Nugues' research is focused on natural language processing for advanced user interfaces and spoken dialogue. This includes the design and the implementation of conversational agents within a multimodal framework and text visualization. He led the team that designed a navigation agent - Ulysse - that enables a user to navigate in a virtual reality environment using language, and the team thatdesignedthe CarSim system that generates animated 3D scenes from written texts.

Pierre Nugues has taught natural-language processing and computational linguistics at the following institutions:ISMRA, Caen, France;University of Nottigham, UK;Staffordshire University, UK; FH Konstanz, Germany;Lund University, Sweden;and Ghent University, Belgium.

1 An Overview of Language Processing	p. 1
1.1 Linguistics and Language Processing	p. 1
1.2 Applications of Language Processing	p. 2
1.3 The Different Domains of Language Processing	p. 3
1.4 Phonetics	p. 4
1.5 Lexicon and Morphology	p. 6
1.6 Syntax	p. 8
1.6.1 Syntax as Defined by Noam Chomsky	p. 8
1.6.2 Syntax as Relations and Dependencies	p. 10
1.7 Semantics	p. 11
1.8 Discourse and Dialogue	p. 14
1.9 Why Speech and Language Processing Are Difficult	p. 14
1.9.1 Ambiguity	p. 15
1.9.2 Models and Their Implementation	p. 16
1.10 An Example of Language Technology in Action: the Persona Project	p. 17
1.10.1 Overview of Persona	p. 17
1.10.2 The Persona's Modules	p. 18
1.11 Further Reading	p. 19
2 Corpus Processing Tools	p. 23
2.1 Corpora	p. 23
2.1.1 Types of Corpora	p. 23
2.1.2 Corpora and Lexicon Building	p. 24
2.1.3 Corpora as Knowledge Sources for the Linguist	p. 26
2.2 Finite-State Automata	p. 27
2.2.1 A Description	p. 27
2.2.2 Mathematical Definition of Finite-State Automata	p. 28
2.2.3 Finite-State Automata in Prolog	p. 29
2.2.4 Deterministic and Nondeterministic Automata	p. 30
2.2.5 Building a Deterministic Automata from a Nondeterministic One	p. 31
2.2.6 Searching a String with a Finite-State Automaton	p. 31
2.2.7 Operations on Finite-State Automata	p. 33
2.3 Regular Expressions	p. 35
2.3.1 Repetition Metacharacters	p. 36
2.3.2 The Longest Match	p. 37
2.3.3 Character Classes	p. 38
2.3.4 Nonprintable Symbols or Positions	p. 39
2.3.5 Union and Boolean Operators	p. 41
2.3.6 Operator Combination and Precedence	p. 41
2.4 Programming with Regular Expressions	p. 42
2.4.1 Perl	p. 42
2.4.2 Matching	p. 42
2.4.3 Substitutions	p. 43
2.4.4 Translating Characters	p. 44
2.4.5 String Operators	p. 44
2.4.6 Back References	p. 45
2.5 Finding Concordances	p. 46
2.5.1 Concordances in Prolog	p. 46
2.5.2 Concordances in Perl	p. 48
2.6 Approximate String Matching	p. 50
2.6.1 Edit Operations	p. 50
2.6.2 Minimum Edit Distance	p. 51
2.6.3 Searching Edits in Prolog	p. 54
2.7 Further Reading	p. 55
3 Encoding, Entropy, and Annotation Schemes	p. 59
3.1 Encoding Texts	p. 59
3.2 Character Sets	p. 60
3.2.1 Representing Characters	p. 60
3.2.2 Unicode	p. 61
3.2.3 The Unicode Encoding Schemes	p. 63
3.3 Locales and Word Order	p. 66
3.3.1 Presenting Time, Numerical Information, and Ordered Words	p. 66
3.3.2 The Unicode Collation Algorithm	p. 67
3.4 Markup Languages	p. 69
3.4.1 A Brief Background	p. 69
3.4.2 An Outline of XML	p. 69
3.4.3 Writing a DTD	p. 71
3.4.4 Writing an XML Document	p. 74
3.4.5 Namespaces	p. 75
3.5 Codes and Information Theory	p. 76
3.5.1 Entropy	p. 76
3.5.2 Huffman Encoding	p. 77
3.5.3 Cross Entropy	p. 80
3.5.4 Perplexity and Cross Perplexity	p. 81
3.6 Entropy and Decision Trees	p. 82
3.6.1 Decision Trees	p. 82
3.6.2 Inducing Decision Trees Automatically	p. 82
3.7 Further Reading	p. 84
4 Counting Words	p. 87
4.1 Counting Words and Word Sequences	p. 87
4.2 Words and Tokens	p. 87
4.2.1 What Is a Word?	p. 87
4.2.2 Breaking a Text into Words: Tokenization	p. 88
4.3 Tokenizing Texts	p. 89
4.3.1 Tokenizing Texts in Prolog	p. 89
4.3.2 Tokenizing Texts in Perl	p. 91
4.4 N-grams	p. 92
4.4.1 Some Definitions	p. 92
4.4.2 Counting Unigrams in Prolog	p. 93
4.4.3 Counting Unigrams with Perl	p. 93
4.4.4 Counting Bigrams with Perl	p. 95
4.5 Probabilistic Models of a Word Sequence	p. 95
4.5.1 The Maximum Likelihood Estimation	p. 95
4.5.2 Using ML Estimates with Nineteen Eighty-Four	p. 97
4.6 Smoothing N-gram Probabilities	p. 99
4.6.1 Sparse Data	p. 99
4.6.2 Laplace's Rule	p. 100
4.6.3 Good-Turing Estimation	p. 101
4.7 Using N-grams of Variable Length	p. 102
4.7.1 Linear Interpolation	p. 103
4.7.2 Back-off	p. 104
4.8 Quality of a Language Model	p. 104
4.8.1 Intuitive Presentation	p. 104
4.8.2 Entropy Rate	p. 105
4.8.3 Cross Entropy	p. 105
4.8.4 Perplexity	p. 106
4.9 Collocations	p. 106
4.9.1 Word Preference Measurements	p. 107
4.9.2 Extracting Collocations with Perl	p. 108
4.10 Application: Retrieval and Ranking of Documents on the Web	p. 109
4.11 Further Reading	p. 111
5 Words, Parts of Speech, and Morphology	p. 113
5.1 Words	p. 113
5.1.1 Parts of Speech	p. 113
5.1.2 Features	p. 114
5.1.3 Two Significant Parts of Speech: The Noun and the Verb	p. 115
5.2 Lexicons	p. 117
5.2.1 Encoding a Dictionary	p. 119
5.2.2 Building a Trie in Prolog	p. 121
5.2.3 Finding a Word in a Trie	p. 123
5.3 Morphology	p. 123
5.3.1 Morphemes	p. 123
5.3.2 Morphs	p. 124
5.3.3 Inflection and Derivation	p. 125
5.3.4 Language Differences	p. 129
5.4 Morphological Parsing	p. 130
5.4.1 Two-Level Model of Morphology	p. 130
5.4.2 Interpreting the Morphs	p. 131
5.4.3 Finite-State Transducers	p. 131
5.4.4 Conjugating a French Verb	p. 133
5.4.5 Prolog Implementation	p. 134
5.4.6 Ambiguity	p. 136
5.4.7 Operations on Finite-State Transducers	p. 137
5.5 Morphological Rules	p. 138
5.5.1 Two-Level Rules	p. 138
5.5.2 Rules and Finite-State Transducers	p. 139
5.5.3 Rule Composition: An Examplewith French Irregular Verbs	p. 141
5.6 Application Examples	p. 142
5.7 Further Reading	p. 142
6 Part-of-Speech Tagging Using Rules	p. 147
6.1 Resolving Part-of-Speech Ambiguity	p. 147
6.1.1 A Manual Method	p. 147
6.1.2 Which Method to Use to Automatically Assign Parts of Speech	p. 147
6.2 Tagging with Rules	p. 149
6.2.1 Brill's Tagger	p. 149
6.2.2 Implementation in Prolog	p. 151
6.2.3 Deriving Rules Automatically	p. 153
6.2.4 Confusion Matrices	p. 154
6.3 Unknown Words	p. 154
6.4 Standardized Part-of-Speech Tagsets	p. 156
6.4.1 Multilingual Part-of-Speech Tags	p. 156
6.4.2 Parts of Speechfor English	p. 158
6.4.3 An Annotation Schemefor Swedish	p. 160
6.5 Further Reading	p. 162
7 Part-of-Speech Tagging Using Stochastic Techniques	p. 163
7.1 The Noisy Channel Model	p. 163
7.1.1 Presentation	p. 163
7.1.2 The N-gram Approximation	p. 164
7.1.3 Tagging a Sentence	p. 165
7.1.4 The Viterbi Algorithm: An Intuitive Presentation	p. 166
7.2 Markov Models	p. 167
7.2.1 Markov Chains	p. 167
7.2.2 Hidden Markov Models	p. 169
7.2.3 Three Fundamental Algorithms to Solve Problems with HMMs	p. 170
7.2.4 The Forward Procedure	p. 171
7.2.5 Viterbi Algorithm	p. 173
7.2.6 The Backward Procedure	p. 174
7.2.7 The Forward-Backward Algorithm	p. 175
7.3 Tagging with Decision Trees	p. 177
7.4 Unknown Words	p. 179
7.5 An Application of the Noisy Channel Model: Spell Checking	p. 179
7.6 A Second Application: Language Models for Machine Translation	p. 180
7.6.1 Parallel Corpora	p. 180
7.6.2 Alignment	p. 181
7.6.3 Translation	p. 183
7.7 Further Reading	p. 184
8 Phrase-Structure Grammars in Prolog	p. 185
8.1 Using Prolog to Write Phrase-Structure Grammars	p. 185
8.2 Representing Chomsky's Syntactic Formalism in Prolog	p. 185
8.2.1 Constituents	p. 185
8.2.2 Tree Structures	p. 186
8.2.3 Phrase-Structure Rules	p. 187
8.2.4 The Definite Clause Grammar (DCG) Notation	p. 188
8.3 Parsing with DCGs	p. 190
8.3.1 Translating DCGs into Prolog Clauses	p. 190
8.3.2 Parsing and Generation	p. 192
8.3.3 Left-Recursive Rules	p. 193
8.4 Parsing Ambiguity	p. 194
8.5 Using Variables	p. 196
8.5.1 Gender and Number Agreement	p. 196
8.5.2 Obtaining the Syntactic Structure	p. 198
8.6 Application: Tokenizing Texts Using DCG Rules	p. 200
8.6.1 Word Breaking	p. 200
8.6.2 Recognition of Sentence Boundaries	p. 201
8.7 Semantic Representation	p. 202
8.7.1 A-Calculus	p. 202
8.7.2 Embedding A-Expressions into DCG Rules	p. 203
8.7.3 Semantic Composition of Verbs	p. 205
8.8 An Application of Phrase-Structure Grammars and a Worked Example	p. 206
8.9 Further Reading	p. 210
9 Partial Parsing	p. 213
9.1 Is Syntax Necessary?	p. 213
9.2 Word Spotting and Template Matching	p. 213
9.2.1 ELIZA	p. 213
9.2.2 Word Spotting in Prolog	p. 214
9.3 Multiword Detection	p. 217
9.3.1 Multiwords	p. 217
9.3.2 AStandard Multiword Annotation	p. 217
9.3.3 Detecting Multiwords with Rules	p. 219
9.3.4 The Longest Match	p. 219
9.3.5 Running the Program	p. 220
9.4 Noun Groups and Verb Groups	p. 222
9.4.1 Groups Versus Recursive Phrases	p. 223
9.4.2 DCG Rules to Detect Noun Groups	p. 223
9.4.3 DCG Rules to Detect Verb Groups	p. 225
9.4.4 Running the Rules	p. 226
9.5 Group Detection as a Tagging Problem	p. 227
9.5.1 Tagging Gaps	p. 227
9.5.2 Tagging Words	p. 228
9.5.3 Using Symbolic Rules	p. 229
9.5.4 Using Statistical Tagging	p. 229
9.6 Cascading Partial Parsers	p. 230
9.7 Elementary Analysis of Grammatical Functions	p. 231
9.7.1 Main Functions	p. 231
9.7.2 Extracting Other Groups	p. 232
9.8 An Annotation Scheme for Groups in French	p. 235
9.9 Application: The FASTUS System	p. 237
9.9.1 The Message Understanding Conferences	p. 237
9.9.2 The Syntactic Layers of the FASTUS System	p. 238
9.9.3 Evaluationof Information Extraction Systems	p. 239
9.10 Further Reading	p. 240
10 Syntactic Formalisms	p. 243
10.1 Introduction	p. 243
10.2 Chomsky's Grammar in Syntactic Structures	p. 244
10.2.1 Constituency: A Formal Definition	p. 244
10.2.2 Transformations	p. 246
10.2.3 Transformations and Movements	p. 248
10.2.4 Gap Threading	p. 248
10.2.5 Gap Threading to Parse Relative Clauses	p. 250
10.3 Standardized Phrase Categories for English	p. 252
10.4 Unification-Based Grammars	p. 254
10.4.1 Features	p. 254
10.4.2 Representing Features in Prolog	p. 255
10.4.3 A Formalism for Features and Rules	p. 257
10.4.4 Features Organization	p. 258
10.4.5 Features and Unification	p. 260
10.4.6 A Unification Algorithm for Feature Structures	p. 261
10.5 Dependency Grammars	p. 263
10.5.1 Presentation	p. 263
10.5.2 Properties of a Dependency Graph	p. 266
10.5.3 Valence	p. 268
10.5.4 Dependencies and Functions	p. 270
10.6 Further Reading	p. 273
11 Parsing Techniques	p. 277
11.1 Introduction	p. 277
11.2 Bottom-up Parsing	p. 278
11.2.1 The Shift-Reduce Algorithm	p. 278
11.2.2 Implementing Shift-Reduce Parsing in Prolog	p. 279
11.2.3 Differences Between Bottom-up and Top-down Parsing	p. 281
11.3 Chart Parsing	p. 282
11.3.1 Backtracking and Efficiency	p. 282
11.3.2 Structure of a Chart	p. 282
11.3.3 The Active Chart	p. 283
11.3.4 Modules of an Earley Parser	p. 285
11.3.5 The Earley Algorithm in Prolog	p. 288
11.3.6 The Earley Parser to Handle Left-Recursive Rules and Empty Symbols	p. 293
11.4 Probabilistic Parsing of Context-Free Grammars	p. 294
11.5 A Description of PCFGs	p. 294
11.5.1 The Bottom-up Chart	p. 297
11.5.2 The Cocke-Younger-Kasami Algorithm in Prolog	p. 298
11.5.3 Adding Probabilities to the CYK Parser	p. 300
11.6 Parser Evaluation	p. 301
11.6.1 Constituency-Based Evaluation	p. 301
11.6.2 Dependency-Based Evaluation	p. 302
11.6.3 PerformanceofPCFG Parsing	p. 302
11.7 Parsing Dependencies	p. 303
11.7.1 Dependency Rules	p. 304
11.7.2 Extending the Shift-Reduce Algorithm to Parse Dependencies	p. 305
11.7.3 Nivre's Parser in Prolog	p. 306
11.7.4 Finding Dependencies Using Constraints	p. 309
11.7.5 Parsing Dependencies Using Statistical Techniques	p. 310
11.8 Further Reading	p. 313
12 Semantics and Predicate Logic	p. 317
12.1 Introduction	p. 317
12.2 Language Meaning and Logic: An Illustrative Example	p. 317
12.3 Formal Semantics	p. 319
12.4 First-Order Predicate Calculus to Represent the State of Affairs	p. 319
12.4.1 Variables and Constants	p. 320
12.4.2 Predicates	p. 320
12.5 Querying the Universe of Discourse	p. 322
12.6 Mapping Phrases onto Logical Formulas	p. 322
12.6.1 Representing Nouns and Adjectives	p. 323
12.6.2 Representing Noun Groups	p. 324
12.6.3 Representing Verbs and Prepositions	p. 324
12.7 The Case of Determiners	p. 325
12.7.1 Determiners and Logic Quantifiers	p. 325
12.7.2 Translating Sentences Using Quantifiers	p. 326
12.7.3 A General Representation of Sentences	p. 327
12.8 Compositionality to Translate Phrases to Logical Forms	p. 329
12.8.1 Translating the Noun Phrase	p. 329
12.8.2 Translating the Verb Phrase	p. 330
12.9 Augmenting the Database and Answering Questions	p. 331
12.9.1 Declarations	p. 332
12.9.2 Questions with Existential and Universal Quantifiers	p. 332
12.9.3 Prolog and Unknown Predicates	p. 334
12.9.4 Other Determiners and Questions	p. 335
12.10 Application: The Spoken Language Translator	p. 335
12.10.1 Translating Spoken Sentences	p. 335
12.10.2 Compositional Semantics	p. 336
12.10.3 Semantic Representation Transfer	p. 338
12.11 Further Reading	p. 340
13 Lexical Semantics	p. 343
13.1 Beyond Formal Semantics	p. 343
13.1.1 La langue etlaparole	p. 343
13.1.2 Language and the Structure of the World	p. 343
13.2 Lexical Structures	p. 344
13.2.1 Some Basic Terms and Concepts	p. 344
13.2.2 Ontological Organization	p. 344
13.2.3 Lexical Classes and Relations	p. 345
13.2.4 Semantic Networks	p. 347
13.3 Building a Lexicon	p. 347
13.3.1 The Lexicon and Word Senses	p. 349
13.3.2 Verb Models	p. 350
13.3.3 Definitions	p. 351
13.4 An Example of Exhaustive Lexical Organization: Word Net	p. 352
13.4.1 Nouns	p. 353
13.4.2 Adjectives	p. 354
13.4.3 Verbs	p. 355
13.5 Automatic Word Sense Disambiguation	p. 356
13.5.1 Senses as Tags	p. 356
13.5.2 Associating a Word with a Context	p. 357
13.5.3 Guessing the Topic	p. 357
13.5.4 Naive Bayes	p. 358
13.5.5 Using Constraints on Verbs	p. 359
13.5.6 Using Dictionary Definitions	p. 359
13.5.7 An Unsupervised Algorithm to Tag Senses	p. 360
13.5.8 Senses and Languages	p. 362
13.6 Case Grammars	p. 363
13.6.1 Cases in Latin	p. 363
13.6.2 Cases and Thematic Roles	p. 364
13.6.3 Parsing with Cases	p. 365
13.6.4 Semantic Grammars	p. 366
13.7 Extending Case Grammars	p. 367
13.7.1 Frame Net	p. 367
13.7.2 A Statistical Method to Identify Semantic Roles	p. 368
13.8 An Example of Case Grammar Application: EVAR	p. 371
13.8.1 EVAR's Ontology and Syntactic Classes	p. 371
13.8.2 Cases in EVAR	p. 373
13.9 Further Reading	p. 373
14 Discourse	p. 377
14.1 Introduction	p. 377
14.2 Discourse: A Minimalist Definition	p. 378
14.2.1 A Description of Discourse	p. 378
14.2.2 Discourse Entities	p. 378
14.3 References: An Application-Oriented View	p. 379
14.3.1 References and Noun Phrases	p. 379
14.3.2 Finding Names - Proper Nouns	p. 380
14.4 Coreference	p. 381
14.4.1 Anaphora	p. 381
14.4.2 Solving Coreferences in an Example	p. 382
14.4.3 A Standard Coreference Annotation	p. 383
14.5 References: A More Formal View	p. 384
14.5.1 Generating Discourse Entities: The Existential Quantifier	p. 384
14.5.2 Retrieving Discourse Entities: Definite Descriptions	p. 385
14.5.3 Generating Discourse Entities: The Universal Quantifier	p. 386
14.6 Centering: A Theory on Discourse Structure	p. 387
14.7 Solving Coreferences	p. 388
14.7.1 A Simplistic Method: Using Syntactic and Semantic Compatibility	p. 389
14.7.2 Solving Coreferences with Shallow Grammatical Information	p. 390
14.7.3 Salience in a Multimodal Context	p. 391
14.7.4 Using a Machine-Learning Technique to Resolve Coreferences	p. 391
14.7.5 More Complex Phenomena: Ellipses	p. 396
14.8 Discourse and Rhetoric	p. 396
14.8.1 Ancient Rhetoric: An Outline	p. 397
14.8.2 Rhetorical Structure Theory	p. 397
14.8.3 Types of Relations	p. 399
14.8.4 Implementing Rhetorical Structure Theory	p. 400
14.9 Events and Time	p. 401
14.9.1 Events	p. 403
14.9.2 Event Types	p. 404
14.9.3 Temporal Representation of Events	p. 404
14.9.4 Events and Tenses	p. 406
14.10 Time ML, an Annotation Scheme for Time and Events	p. 407
14.11 Further Reading	p. 409
15 Dialogue	p. 411
15.1 Introduction	p. 411
15.2 Why a Dialogue?	p. 411
15.3 Simple Dialogue Systems	p. 412
15.3.1 Dialogue Systems Based on Automata	p. 412
15.3.2 Dialogue Modeling	p. 413
15.4 Speech Acts: A Theory of Language Interaction	p. 414
15.5 Speech Acts and Human-Machine Dialogue	p. 417
15.5.1 Speech Acts as a Tagging Model	p. 417
15.5.2 Speech Acts Tags Used in the SUNDIAL Project	p. 418
15.5.3 Dialogue Parsing	p. 419
15.5.4 Interpreting Speech Acts	p. 421
15.5.5 EVAR: A Dialogue Application Using Speech Acts	p. 422
15.6 Taking Beliefs and Intentions into Account	p. 423
15.6.1 Representing Mental States	p. 425
15.6.2 The STRIPS Planning Algorithm	p. 427
15.6.3 Causality	p. 429
15.7 Further Reading	p. 430
A An Introduction to Prolog	p. 433
A.1 A Short Background	p. 433
A.2 Basic Features of Prolog	p. 434
A.2.1 Facts	p. 434
A.2.2 Terms	p. 435
A.2.3 Queries	p. 437
A.2.4 Logical Variables	p. 437
A.2.5 Shared Variables	p. 438
A.2.6 Data Types in Prolog	p. 439
A.2.7 Rules	p. 440
A.3 Running a Program	p. 442
A.4 Unification	p. 443
A.4.1 Substitution and Instances	p. 443
A.4.2 Terms and Unification	p. 444
A.4.3 The Herbrand Unification Algorithm	p. 445
A.4.4 Example	p. 445
A.4.5 The Occurs-Check	p. 446
A.5 Resolution	p. 447
A.5.1 Modus Ponens	p. 447
A.5.2 A Resolution Algorithm	p. 447
A.5.3 Derivation Trees and Backtracking	p. 448
A.6 Tracing and Debugging	p. 450
A.7 Cuts, Negation, and Related Predicates	p. 452
A.7.1 Cuts	p. 452
A.7.2 Negation	p. 453
A.7.3 The once/1 Predicate	p. 454
A.8 Lists	p. 455
A.9 Some List-Handling Predicates	p. 456
A.9.1 The member/2 Predicate	p. 456
A.9.2 The append/3 Predicate	p. 457
A.9.3 The delete/3 Predicate	p. 458
A.9.4 The intersection/3 Predicate	p. 458
A.9.5 The reverse/2 Predicate	p. 459
A.9.6 The Mode of an Argument	p. 459
A.10 Operators and Arithmetic	p. 460
A.10.1 Operators	p. 460
A.10.2 Arithmetic Operations	p. 460
A.10.3 Comparison Operators	p. 462
A.10.4 Lists and Arithmetic: The length/2 Predicate	p. 463
A.10.5 Lists and Comparison: The quicksort/2 Predicate	p. 463
A.11 Some Other Built-in Predicates	p. 464
A.11.1 Type Predicates	p. 464
A.11.2 Term Manipulation Predicates	p. 465
A.12 Handling Run-Time Errors and Exceptions	p. 466
A.13 Dynamically Accessing and Updatingthe Database	p. 467
A.13.1 Accessing a Clause: The clause/2 Predicate	p. 467
A.13.2 Dynamic and Static Predicates	p. 468
A.13.3 Adding a Clause: The asserta/1 and 1 assertz/Predicates	p. 468
A.13.4 Removing Clauses: The retract/1 and abolish/2 Predicates	p. 469
A.13.5 Handling Unknown Predicates	p. 470
A.14 All-Solutions Predicates	p. 470
A.15 Fundamental Search Algorithms	p. 471
A.15.1 Representing the Graph	p. 472
A.15.2 Depth-First Search	p. 473
A.15.3 Breadth-First Search	p. 474
A.15.4 A* Search	p. 475
A.16 Input/Output	p. 476
A.16.1 Reading and Writing Characters with Edinburgh Prolog	p. 476
A.16.2 Reading and Writing Terms with Edinburgh Prolog	p. 476
A.16.3 Opening and Closing Files with Edinburgh Prolog	p. 477
A.16.4 Reading and Writing Characters with Standard Prolog	p. 478
A.16.5 Reading and Writing Terms with Standard Prolog	p. 479
A.16.6 Opening and Closing Files with Standard Prolog	p. 479
A.16.7 Writing Loops	p. 480
A.17 Developing Prolog Programs	p. 481
A.17.1 Presentation Style	p. 481
A.17.2 Improving Programs	p. 482
Index	p. 487
References	p. 497

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