Integration of World Knowledge for Natural Language Understanding


Integration of World Knowledge for Natural Language Understanding (2012) .. by E. Ovchinnikova


Contents

Foreword v
Acknowledgments vii
List of Figures xiii
List of Tables xv
List of Algorithms xvii

1. Preliminaries 1
1.1 Introduction … 1
1.2 Objectives … 4
1.3 How to Read This Book … 13

2. Natural Language Understanding and World Knowledge 15
2.1 What is Natural Language Understanding? … 15
2.2 Representation of Meaning … 18
2.2.1 Meaning Representation in Linguistic Theories … 19
2.2.2 Linguistic Meaning in Artificial Intelligence … 26
2.3 Shared Word Knowledge for Natural Language Understanding … 30
2.3.1 Linguistic vs. World Knowledge … 31
2.3.2 Natural Language Phenomena Requiring Word Knowledge to be Resolved 33
2.4 Concluding Remarks … 36

3. Sources of World Knowledge 39
3.1 Lexical-semantic Resources … 40
3.1.1 Hand-crafted Electronic Dictionaries … 46
3.1.2 Automatically Generated Lexical-semantic Databases … 53
3.2 Ontologies … 56
3.2.1 Foundational Ontologies … 59
3.2.2 Domain-specific Ontologies … 64
3.3 Mixed Resources … 65
3.3.1 Ontologies Learned from Text … 65
3.3.2 Ontologies Learned from Structured Sources: YAGO … 66
3.3.3 Ontologies Generated Using Community Efforts: ConceptNet … 67
3.4 Concluding Remarks … 68

4. Reasoning for Natural Language Understanding 73
4.1 Semantic Parsers … 74
4.1.1 English Slot Grammar … 74
4.1.2 Boxer … 76
4.2 Deduction for Natural Language Understanding … 77
4.3 Abduction for Natural Language Understanding … 81
4.4 Reasoning with Description Logics … 86
4.5 Concluding Remarks … 90

5. Knowledge Base Construction 93
5.1 Preliminaries … 95
5.1.1 Notation … 95
5.1.2 Axiom Weights … 95
5.2 Axioms derived from Lexical-Semantic Resources … 96
5.2.1 Axioms derived from WordNet … 96
5.2.2 Axioms derived from FrameNet … 101
5.2.3 Axioms derived from Proposition Store … 114
5.3 Ontology … 116
5.4 Similarity Space … 118
5.5 Concluding Remarks … 121

6. Ensuring Consistency 123
6.1 Conceptual Inconsistency of Frame Relations … 124
6.1.1 Ontological Status of Frames … 126
6.1.2 Constraints on Frame Relations … 128
6.1.3 Reasoning-Related Conceptual Problems in FrameNet … 131
6.1.4 Case Study … 133
6.2 Logical Inconsistency in Ontologies … 134
6.2.1 Resolving Logical Inconsistencies … 136
6.2.2 Tracing Clashes … 139
6.2.3 Resolving Overgeneralization … 141
6.2.4 Root and Derived Concepts … 145
6.2.5 Rewriting Algorithm … 147
6.2.6 Prototypical Implementation … 151
6.3 Concluding Remarks … 153

7. Abductive Reasoning with the Integrative Knowledge Base 155
7.1 Adapting Mini-TACITUS to a Large Knowledge Base … 155
7.2 Refining Abductive Reasoning Procedure for NLU … 162
7.3 Reasoning with Ontologies … 166
7.4 Reasoning with Similarity Space … 171
7.5 Concluding Remarks … 173

8. Evaluation 177
8.1 Natural Language Understanding Tasks … 179
8.1.1 Recognizing Textual Entailment … 179
8.1.2 Semantic Role Labeling … 182
8.1.3 Paraphrasing of Noun Dependencies … 185
8.2 Experiments with Boxer and Nutcracker … 187
8.2.1 Recognizing Textual Entailment … 189
8.2.2 Semantic Role Labeling … 194
8.3 Experiments with Mini-TACITUS … 195
8.3.1 Recognizing Textual Entailment … 196
8.3.2 Semantic Role Labeling … 201
8.3.3 Paraphrasing of Noun Dependencies … 202
8.3.4 Domain Text Interpretation … 207
8.4 Concluding Remarks … 212

9. Conclusion 215

Appendix A 221
Bibliography 225
Index 241

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