In Ontological Semantics, Sergei Nirenburg and Victor Raskin introduce a comprehensive approach to the treatment of text meaning by computer. Arguing that being able to use meaning is crucial to the success of natural language processing (NLP) applications, they depart from the ad hoc approach to meaning taken by much of the NLP community and propose theory-based semantic methods. Ontological semantics, an integrated complex of theories, methodologies, descriptions, and implementations, attempts to systematize ideas about both semantic description as representation and manipulation of meaning by computer programs. It is built on already coordinated "microtheories" covering such diverse areas as specific language phenomena, processing heuristics, and implementation system architecture rather than on isolated components requiring future integration. Ontological semantics is constantly evolving, driven by the need to make meaning manipulation tasks such as text analysis and text generation work. Nirenburg and Raskin have therefore developed a set of heterogeneous methods suited to a particular task and coordinated at the level of knowledge acquisition and runtime system architecture implementations, a methodology that also allows for a variable level of automation in all its processes.
Nirenburg and Raskin first discuss ontological semantics in relation to other fields, including cognitive science and the AI paradigm, the philosophy of science, linguistic semantics and the philosophy of language, computational lexical semantics, and studies in formal ontology. They then describe the content of ontological semantics, discussing text-meaning representation, static knowledge sources (including the ontology, the fact repository, and the lexicon), the processes involved in text analysis, and the acquisition of static knowledge.
Author(s): Sergei Nirenburg, Victor Raskin
Series: Language, Speech, and Communication
Edition: illustrated edition
Publisher: The MIT Press
Year: 2004
Language: English
Pages: 329
9.3.2 Paradigmatic Approach to Semantic Acquisition I: “Rapid Propagation”......Page 3
conclusion.pdf......Page 0
1. Introduction to Ontological Semantics......Page 7
1.1.1 Relevant Components of an Intelligent Agent’s Model......Page 12
1.1.3 Operation of the Discourse Consumer......Page 13
1.2 Ontological Semantics: An Initial Sketch......Page 14
1.3 Ontological Semantics and Non-Semantic NLP Processors......Page 16
1.4 Architectures for Comprehensive NLP Applications......Page 17
1.4.1 The Stratified Model......Page 18
1.4.3 Toward Constraint Satisfaction Architectures......Page 19
1.5.1 The Analyzer......Page 23
1.5.3 World Knowledge Maintenance and Reasoning Module......Page 24
1.6 The Static Knowledge Sources......Page 25
1.7 The Concept of Microtheories......Page 26
2.1 Reasons for Philosophizing......Page 29
2.2.1 Introduction: Philosophy, Science, and Engineering......Page 31
2.2.2 Reason One: Optimization......Page 33
2.2.3 Reason Two: Challenging Conventional Wisdom......Page 34
2.2.4 Reason Three: Standardization and Evaluation......Page 35
2.3 Components of a Theory......Page 36
2.3.2 Premises......Page 38
2.3.3 Body......Page 40
2.3.4 Justification......Page 41
2.4 Parameters of Linguistic Semantic Theories......Page 43
2.4.1.1 Adequacy......Page 44
2.4.1.2 Effectiveness......Page 45
2.4.1.3 Explicitness......Page 47
2.4.1.4 Formality and Formalism......Page 48
2.4.2.1 Methodology and Linguistic Theory......Page 50
2.4.2.4 Methodology of Discovery: Heuristics......Page 52
2.4.2.5 Practical Skills and Tools as Part of Methodology......Page 54
2.4.2.6 Disequilibrium Between Theory and Methodology......Page 55
2.4.4 Parameters Related to the Internal Organization of a Theory......Page 56
2.4.5 Parameter Values and Some Theories......Page 57
2.5.1 Theories and Applications......Page 60
2.5.1.1 Difference 1: Goals......Page 64
2.5.2 Blame Assignment......Page 65
2.5.3.2 Solutions are a Must, Even for Unsolvable Problems......Page 66
2.5.4.2 Partial Interactions......Page 67
2.5.4.4 Constraints on Automation......Page 68
2.5.5.1 Statistics-Based Machine Translation......Page 69
2.5.5.2 Quick Ramp-Up Machine Translation Developer System......Page 70
2.6 Using the Parameters......Page 73
2.6.1 Purview......Page 74
8.2.2 Matching Selectional Restrictions......Page 75
9.3.1 General Principles of Lexical Semantic Acquisition......Page 77
2.6.3 Justification......Page 78
8.2.3 Multivalued Static Selectional Restrictions......Page 79
9.3.3 Paradigmatic Approach to Lexical Acquisition II: Lexical Rules......Page 80
3.2 Diachrony of word meaning......Page 83
3.3 Meaning and reference.......Page 85
3.4.1 Option 1: Refusing to Study Meaning......Page 86
3.4.3 Option 3: Componential Analysis, or the Dawn of Metalanguage......Page 87
3.4.4 Option 4: Logic, or Importing a Metalanguage......Page 88
3.5.1 Formal Semantics......Page 90
3.5.2 Semantic vs. Syntactic Compositionality......Page 94
3.5.3 Compositionality in Linguistic Semantics......Page 95
3.6 A Trio of Free-Standing Semantic Ideas from Outside Major Schools......Page 97
3.7 Compositionality in Computational Semantics.......Page 98
4.1.1 Generative Lexicon: Main Idea......Page 101
4.1.2 Generative vs. Enumerative?......Page 102
4.1.3 Generative Lexicon and Novel Senses......Page 103
4.1.4 Permeative Usage?......Page 104
4.2 Syntax vs. Semantics......Page 106
4.3 Lexical Semantics and Sentential Meaning.......Page 108
4.3.2 Ontological Semantics for Sentential Meaning......Page 109
4.3.3 Lexical Semantics and Pragmatics......Page 111
4.4 Description Coverage......Page 112
5.1 Ontology and Metaphysics......Page 117
5.2.1 Formal Basis of Ontology......Page 119
5.2.2 Ontology as Engineering......Page 121
5.2.3 Ontology Interchange......Page 122
5.3.1 A Quick and Dirty Distinction Between Ontology and Natural Language......Page 124
5.3.2 The Real Distinction Between Ontology and Natural Language......Page 126
5.4 A Wish List for Formal Ontology from Ontological Semantics......Page 130
9. Acquisition of Static Knowledge Sources for Ontological Semantics......Page 132
6.1 Meaning Proper and the Rest......Page 133
9.3.7 Ontological Matching and Lexical Constraints......Page 138
6.3 Ontological Concepts and Non-Ontological Parameters in TMR......Page 145
8.6.1 Reference and Co-Reference......Page 146
6.5 Further Examples of TMR Specification......Page 149
7.3 The Lexicon......Page 152
6.7 Basic and Extended TMRs......Page 153
7.1.1 The Format of Mikrokosmos Ontology......Page 160
7.1.3 Case Roles for Predicates......Page 171
7.1.4 Choices and Trade-Offs in Ontological Representations.......Page 142
7.1.5 Complex Events......Page 143
10. Conclusion......Page 2
7.4 The Onomasticon......Page 202
9.1 Automating Knowledge Acquisition in Ontological Semantics......Page 156
8.1.1 Tokenization and Morphological Analysis......Page 205
8.1.2 Lexical Look-up......Page 161
8.1.3 Syntactic Analysis......Page 208
8.2 Building Basic Semantic Dependency......Page 162
9.2 Acquisition of Ontology......Page 134
9.3.4 Steps in Lexical Acquisition......Page 172
8.3.1 Dynamic Tightening of Selectional Restrictions......Page 218
9.3.6 Grain Size and Practical Effability......Page 137
8.4.2 Processing Non-literal Language......Page 228
8.5.1 Aspect......Page 139
8.5.2 Proposition Time......Page 6
8.5.3 Modality......Page 140
8.6.3 Discourse Relations......Page 148
9.3 Acquisition of Lexicon......Page 76
9.3.5 Polysemy Reduction......Page 277
9.4 Acquisition of Fact DB......Page 175
