Ontological Semantics by Sergei Nirenburg and Victor Raskin
http://crl.nmsu.edu/Staff.pages/Technical/sergei/book/
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作者介绍
二位作者，一位来自马里兰大学巴尔的摩分校，另一位来自普度大学。他们都是语言学出身，所写文章都是关于计算语言学、本体、语义等等。详细介绍如下：
Sergei Nirenburg
Professor - Computer Science & Electrical Engineering
University of Maryland Baltimore County
ECS 202H
1000 Hilltop Circle
Baltimore, MD 21250
phone: (410) 455-3965
email: sergei@cs.umbc.edu
Education:
Ph.D., Linguistics, The Hebrew University of Jerusalem, Israel, 1980
M.Sc., Computational Linguistics, Kharkov State University, Kharkov, USSR, 1974
Employment:
2002 - Present, Professor, UMBC, Computer Science & Electrical Engineering
1994 - 2002, Computing Research Laboratory, New Mexico State University, Las Cruces, NM
1994 - 2002, Professor, Department of Computer Science, New Mexico State University
1991 - 1994, Senior Research Computer Scientist, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
1987 - 1991, Research Computer Scientist, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
1984 - 1985, Visiting Assistant Professor, Department of Computer and Information Sciences, University of Massachusetts, Amherst, MA
1982 - 1986, Assistant Professor of Computer Science, Colgate University, Hamilton, NY
1980 - 1982, Junior Lecturer, Department of Computer Science, The Hebrew University of Jerusalem, Israel
Research Areas:
Natural Language Processing, Artificial Intelligence, Knowledge-Based Systems, Machine Translation, Ontological Semantics, Computational Linguistics
Victor Raskin
Professor of English and Linguistics
vraskin@purdue.edu
--------------------------------------------------------------------------------
Education:
1970 Ph.D. in Structural, Computational, and Mathematical Linguistics, Moscow State University, USSR
1966 M.A./M.S. summa cum laude in Structural and Computational Linguistics, Moscow State University, USSR
1964 B.A./B.S. in Structural and Computational Linguistics, Moscow State University, USSR
Experience:
1999-present Editor-at-Large, HUMOR: International Journal of Humor Research
1998-present Charter Member, Internal Advisory Board, Center for Education and Research in Information Assurance and Security (CERIAS), Purdue University
1994-present PI- and VP-level consultant on natural language and information technology for research laboratories and businesses
1980-present Professor of English and Linguistics, Purdue University
2000 President, International Society of Humor Research
1995-99 Chair, Graduate Interdepartmental Program in Linguistics, Purdue University
1987-99 Editor-in-Chief, HUMOR: International Journal of Humor Research
1979-99 Chair, Interdepartmental Program in Linguistics, Purdue University
1979-80 Associate Professor of English and Linguistics, Purdue University
1978-79 Associate Professor of English, Purdue University
1978 Visiting Professor of Linguistics, University of Michigan
1973-78 Senior Associate Professor of Russian and Philosophy, The Hebrew University of Jerusalem, Israel
1973-78 Senior Associate Professor of Linguistics (half-time), Tel Aviv University, Israel
1966-73 From Lecturer to Acting Associate Professor of Linguistics, Moscow State University
1962-73 From Junior Assistant to Group Leader, Computational Linguistics Lab, Moscow State University
Research Interests & Background:
Linguistic and semantic theory and their applications
Natural language processing (NLP) and its business applications
Ontology
Computational semantics
World and lexical knowledge acquisition
Humor Theory
For a complete curriculum vitae, click here. It's long and never quite up to date, so click through a lot of nonsense to get to the publications. Much work in NLP is already downloadable from crl.nmsu.edu/Research/Projects/Mikrokosmos. To download (in Acrobat PDF format) one of the works below, click on its title:
Ontological Semantics, 2001 (forthcoming). With S. Nirenburg.
Prolegomena to the Philosophy of Linguistics. MCCS-99-318, 1999. With S. Nirenburg.
Natural Language Processing for Information Assurance and Security: An Overview and Implementations. With M. J. Atallah, C. J. McDonough, and S. Nirenburg). Published in: M. Schaefer (ed.), Proceedings. New Security Paradigm Workshop. September 18th-22nd, 2000, Ballycotton, County, Cork Ireland. New York: ACM Press, pp. 51-65.
Natural language watermarking: Design, analysis, and a proof-of-concept implementation. In: Ira S. Moskowitz (ed.), Pre-Proceedings of the 4th Information Hiding Workshop. Pittsburgh: Pittsburgh University Center, 2001, pp. 193-208. With M. J. Atallah and others.
Lexical Semantics of Adjectives: A microtheory of Adjectival Meaning. MCCS-95-288, 1995. With S. Nirenburg.
Ten Choices for Lexical Semantics. MCCS-96-304, 1996. With S. Nirenburg.
Last Modified: October 15, 2002
本书目录如下
Table of Contents
Preface
1. Introduction to Ontological Semantics
1.1 A Model of Language Communication Situation for Ontological Semantic Theory 15
1.1.1 Relevant Components of an Intelligent Agent’s Model 15
1.1.2 Goals and Operation of the Discourse Producer 16
1.1.3 Operation of the Discourse Consumer 16
1.2 Ontological Semantics: An Initial Sketch 17
1.3 Ontological Semantics and Non-Semantic NLP Processors 19
1.4 Architectures for Comprehensive NLP Applications 20
1.4.1 The Stratified Model 21
1.4.2 The “Flat” Model 22
1.4.3 Toward Constraint Satisfaction Architectures 22
1.5 The Major Dynamic Knowledge Sources in Ontological Semantics 26
1.5.1 The Analyzer 26
1.5.2 The Generator 27
1.5.3 World Knowledge Maintenance and Reasoning Module 27
1.6 The Static Knowledge Sources 28
1.7 The Concept of Microtheories 29
2. Prolegomena to the Philosophy of Linguistics
2.1 Reasons for Philosophizing 32
2.2 Reasons for Theorizing 34
2.2.1 Introduction: Philosophy, Science, and Engineering 34
2.2.2 Reason One: Optimization 36
2.2.3 Reason Two: Challenging Conventional Wisdom 37
2.2.4 Reason Three: Standardization and Evaluation 38
2.2.5 Reason Four: Explanation 39
2.2.6 Reason Five: Reusability 39
2.3 Components of a Theory 39
2.3.1 Purview 41
2.3.2 Premises 41
2.3.3 Body 43
2.3.4 Justification 44
2.4 Parameters of Linguistic Semantic Theories 46
2.4.1 Parameters Related to Theory Proper 47
2.4.1.1 Adequacy 47
2.4.1.2 Effectiveness 48
2.4.1.3 Explicitness 50
2.4.1.4 Formality and Formalism 51
2.4.1.5 Ambiguity 53
2.4.2 Parameters Related to the Methodology Associated with a Theory 53
2.4.2.1 Methodology and Linguistic Theory 53
2.4.2.2 Methodology and AI 55
2.4.2.3 Methodology and the Philosophy of Science 55
2.4.2.4 Methodology of Discovery: Heuristics 55
2.4.2.5 Practical Skills and Tools as Part of Methodology 57
2.4.2.6 Disequilibrium Between Theory and Methodology 58
2.4.2.7 Specific Methodology-Related Parameters 59
2.4.3 Parameters Related to the Status of Theory as Model of Human Behavior 59
2.4.4 Parameters Related to the Internal Organization of a Theory 59
2.4.5 Parameter Values and Some Theories 60
2.5 Relations Among Theory, Methodology and Applications 63
2.5.1 Theories and Applications 63
2.5.1.1 Difference 1: Goals 67
2.5.1.2 Difference 2: Attitude to Resources 68
2.5.1.3 Difference 3: Evaluation 68
2.5.2 Blame Assignment 68
2.5.3 Methodologies for Applications 69
2.5.3.1 “Purity” of Methodology 69
2.5.3.2 Solutions are a Must, Even for Unsolvable Problems 69
2.5.4 Aspects of Interactions Among Theories, Applications, and Methodologies 70
2.5.4.1 Explicit Theory Building 70
2.5.4.2 Partial Interactions 70
2.5.4.3 Theoretical Premises Pertaining to Applications 71
2.5.4.4 Constraints on Automation 71
2.5.4.5 Real-Life Interactions 72
2.5.5 Examples of Interactions Among Theories, Applications, and Methodologies 72
2.5.5.1 Statistics-Based Machine Translation 72
2.5.5.2 Quick Ramp-Up Machine Translation Developer System 73
2.6 Using the Parameters 76
2.6.1 Purview 77
2.6.2 Premises 78
2.6.2.1 Premise 1: Meaning Should Be Studied and Represented 78
2.6.2.2 Premise 2: The Need for Ontology 78
2.6.2.3 Premise 3: Machine Tractability 80
2.6.2.4 Premise 4: Qualified Compositionality 80
2.6.3 Justification 81
2.6.3.1 Why should meaning be studied and represented? 82
2.6.3.2 Why is ontology needed? 82
2.6.3.3 Why should meaning be machine tractable? 83
2.6.3.4 Why should meaning be treated as both compositional and non-compositional? 83
2.7 “Post-Empirical” Philosophy of Linguistics 83
3. Ontological Semantics and the Study of Meaning in Linguistics, Philosophy and Computational Linguistics
3.1 Prehistory of semantics 86
3.2 Diachrony of word meaning 86
3.3 Meaning and reference. 88
3.4 The Quest for Meaning Representation I: From Ogden and Richards to Bar-Hillel 89
3.4.1 Option 1: Refusing to Study Meaning 89
3.4.2 Option 2: Semantic Fields, or Avoiding Metalanguage 90
3.4.3 Option 3: Componential Analysis, or the Dawn of Metalanguage 90
3.4.4 Option 4: Logic, or Importing a Metalanguage 91
3.5 The Quest for Meaning Representation II: Contemporary Approaches 93
3.5.1 Formal Semantics 93
3.5.2 Semantic vs. Syntactic Compositionality 97
3.5.3 Compositionality in Linguistic Semantics 98
3.6 A Trio of Free-Standing Semantic Ideas from Outside Major Schools 100
3.7 Compositionality in Computational Semantics. 101
4. Choices for Lexical Semantics
4.1 Generativity 104
4.1.1 Generative Lexicon: Main Idea 104
4.1.2 Generative vs. Enumerative? 105
4.1.3 Generative Lexicon and Novel Senses 106
4.1.4 Permeative Usage? 107
4.1.5 Generative Vs. Enumerative “Yardage” 109
4.2 Syntax vs. Semantics 109
4.3 Lexical Semantics and Sentential Meaning. 111
4.3.1 Formal Semantics for Sentential Meaning 112
4.3.2 Ontological Semantics for Sentential Meaning 112
4.3.3 Lexical Semantics and Pragmatics 114
4.4 Description Coverage 115
5. Formal Ontology and the Needs of Ontological Semantics
5.1 Ontology and Metaphysics 120
5.2 Formal Ontology 122
5.2.1 Formal Basis of Ontology 122
5.2.2 Ontology as Engineering 124
5.2.3 Ontology Interchange 125
5.3 Ontology and Natural Language 127
5.3.1 A Quick and Dirty Distinction Between Ontology and Natural Language 127
5.3.2 The Real Distinction Between Ontology and Natural Language 129
5.4 A Wish List for Formal Ontology from Ontological Semantics 133
6. Meaning Representation in Ontological Semantics
6.1 Meaning Proper and the Rest 136
6.2 TMR in Ontological Semantics 141
6.3 Ontological Concepts and Non-Ontological Parameters in TMR 148
6.4 The Nature and Format of TMR 149
6.5 Further Examples of TMR Specification 152
6.6 Synonymy and Paraphrases 155
6.7 Basic and Extended TMRs 156
7. The Static Knowledge Sources: Ontology, Fact Database and Lexicons
7.1 The Ontology 160
7.1.1 The Format of Mikrokosmos Ontology 163
7.1.2 Inheritance 172
7.1.3 Case Roles for Predicates 174
7.1.4 Choices and Trade-Offs in Ontological Representations. 180
7.1.5 Complex Events 182
7.1.6 Axiomatic definition of ontology. 187
7.2 Fact DB 191
7.3 The Lexicon 195
7.4 The Onomasticon 205
8. Basic Processing in Ontological Semantic Text Analysis
8.1 Preprocessing 208
8.1.1 Tokenization and Morphological Analysis 208
8.1.2 Lexical Look-up 210
8.1.3 Syntactic Analysis 211
8.2 Building Basic Semantic Dependency 212
8.2.1 Establishing Propositional Structure 213
8.2.2 Matching Selectional Restrictions 216
8.2.3 Multivalued Static Selectional Restrictions 218
8.3 When Basic Procedure Returns More Than a Single Answer 221
8.3.1 Dynamic Tightening of Selectional Restrictions 221
8.3.2 When All Else Goes Wrong: Comparing Distances in Ontological Space 228
8.4 When Basic Procedure Returns No Answer 231
8.4.1 Relaxation of Selectional Restrictions 231
8.4.2 Processing Non-literal Language 231
8.4.3 Processing Unattested Inputs 235
8.4.4 Processing Ellipsis 237
8.5 Processing Meaning Beyond Basic Semantic Dependencies 239
8.5.1 Aspect 239
8.5.2 Proposition Time 245
8.5.3 Modality 248
8.6 Processing at the Suprapropositional Level 254
8.6.1 Reference and Co-Reference 254
8.6.2 TMR Time 257
8.6.3 Discourse Relations 258
8.6.4 Style 260
9. Acquisition of Static Knowledge Sources for Ontological Semantics
9.1 Automating Knowledge Acquisition in Ontological Semantics 261
9.2 Acquisition of Ontology 265
9.3 Acquisition of Lexicon 273
9.3.1 General Principles of Lexical Semantic Acquisition 273
9.3.2 Paradigmatic Approach to Semantic Acquisition I: “Rapid Propagation” 274
9.3.3 Paradigmatic Approach to Lexical Acquisition II: Lexical Rules 276
9.3.4 Steps in Lexical Acquisition 280
9.3.5 Polysemy Reduction 280
9.3.6 Grain Size and Practical Effability 287
9.3.7 Ontological Matching and Lexical Constraints 291
9.4 Acquisition of Fact DB 298
10. Conclusion
Bibliography
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一本书《Ontological Semantics 》
