Luận án tiến sĩ: Ngôn ngữ hình ảnh Tropes - Christine M. Keuper

1 Understanding the Figurative Language of Tropes in Natural Language Processing Using a Brain-based Organization for Ontologies by Christine M. Keuper A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate School of Computer and Information Sciences Nova Southeastern University 2007 UMI Number: 3244325 Copyright 2007 by Keuper, Christine M. All rights reserved. UMI Microform 3244325 Copyright 2007 by ProQuest Information and Learning Company.

All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P. Box 1346 Ann Arbor, MI 48106-1346 2 We hereby certify that this dissertation, submitted by Christine M.

Keuper, conforms to acceptable standards and is fully adequate in scope and quality to fulfill the disser- tation requirements for the degree of Doctor of Philosophy. _____________________________________________ ________________ Michael Laszlo, Ph. Date Chairperson of Dissertation Committee _____________________________________________ ________________ James Cannady, Ph. Date Dissertation Committee Member _____________________________________________ ________________ Amon Seagull, Ph.

Date Dissertation Committee Member Approved: _____________________________________________ ________________ Edward Lieblein, Ph. Date Dean, Graduate School of Computer and Information Sciences Graduate School of Computer and Information Sciences Nova Southeastern University 2007 Understanding the Figurative Language of Tropes in Natural Language Processing Using a Brain-based Organization for Ontologies by Christine M. Keuper 2007 Look, love, what envious streaks Do lace the severing clouds in yonder east; Night's candles are burnt out, and jocund day Stands tiptoe on the misty mountain tops. “Romeo and Juliet,” Shakespeare Language communication is the successful interpretation of the speaker’s communicative intent.

When Shakespeare writes, we see the intent in Romeo’s words, but it is lost again when we attempt to express it using a computer model for language; a model with an ability to handle tropes (metaphor, metonymy, synecdoche and irony) is needed. The goal of this model is to correctly interpret the nouns that occur within these tropes. Early computer language models had not worked well when they encountered tropes, yet the brain handled them easily. These early models concentrated on the language functions of the left temporal lobes of the brain; perhaps the models worked poorly because they had limited themselves to modelling only the parts of the brain that handled propositional language.

The designs of these models were also influenced by the assumption that the human brain understood language using a grammar-based Language Acquisition Device. In examining human language acquisition however, grammar does not even show up until the third year. In addition to the common taxonomic and mereologic structures that occur in most language models, the current model also recreates the brain’s thematic, perceptual and functional categorizations. Words no longer occur at a single location: words defined by their perceptual features, whether nouns or adjectives, occur within perceptual categoriza- tions, and those defined by functional features, whether nouns or verbs, occur within functional categorizations.

Tenor-vehicle connections then expand these perceptual and functional categories with metaphor. Words occurring within thematic categories are used to understand metonymy; and words occurring in the taxonomic and mereologic struc- tures are used to understand synecdoche. Classifiers, such as the Japanese hon, indicate membership in a category. Marked percep- tual and functional classifiers in ASL, Japanese and Swahili made it easier to identify the occurrences of unmarked perceptual and functional categories in English.

Likewise, the mythos-based categories in Dyirbal, French and German made the remnants of mythos- based categories still occurring in English understandable. This is one or two pages, page iii or pages iii and iv. The page number(s) should not be printed. The abstract should be written in the past tense.

It should contain the problem statement, method(s) employed, results/findings, conclusions, and recommendations. It Acknowledgments To do successful research, you don’t need to know everything, you just need to know one thing that isn’t known. Art Schawlow I wish to make these acknowledgments in chronological order. To my mother who was there with love and support my entire life.

She is missed now that she is gone. To my father, who had a career in the military that started at 16, who went back to high school and graduated with me, and then went on to college at the same time I did, and graduated with a college diploma the same year I did as well. He is also missed. To my daughter Francie, who was still an infant when this journey began, the day I went across town to the Polytechnic University in San Luis Obispo, California and became part of a very small group of women who wanted to study engineering amongst the thousands of men there.

She taught me about child language acquisition, she was a joyful part of my life and gave me a reason to get up every morning, and she was emotional support to me many years later when we were both in graduate school at the same time. To my older brother Robert who followed me to the university, also to study engineering, but who died after developing a fatal cancer. He always believed in me. To my youngest brother Phillip, who I raised from infancy, who is also no longer here.

To my younger sister Karen, who was always a safety net for me. To my professors at Cal Poly: To Dr. Peter Litchfield, who taught me experimental psychology methodology. Barbara Cook, who taught me cultural anthropology.

Robert Lint, my linguistics professor, for the wonderful sense of déjà vu that occurred when I walked into my first compiler design class, for all of the questions he asked, some of which I am still trying to answer here many years after his death. Jay Bayne, my advisor, and Dr. Emile Attala, my thesis advisor, for encouraging my love of computer science, and all of the fantastical directions I wanted to go with the computer. To Lisa Krasna, who let me adopt and raise her deaf, autistic son, Jeremy.

To Jeremy, who taught me what I didn’t know, and who has become a great joy in my life in his adulthood. Edward Ritvo, for his medical research that opened the doors for Jeremy, and for introducing me to Bill Christopher. To Bill Christopher, who also has an adopted, autistic son, Ned, and who introduced me to Dr. Art Schawlow and his wife Aurelia, who had an autistic son, Artie.

To Art and Aurelia who encouraged me to continue the development of the methodology I used to teach Jeremy language, and who also both encouraged me to continue my studies in computer science. They are both missed. To Alan Alda, who encouraged me to continue development of the sign language dictionary I was working on, and who encouraged me to return to graduate school. Graham Chalmers, my friend and advisor of many years.

To Mark Lucas and Scott Simon, who were always there with new language features for the development environment. John Bonvillian, whose emails helped me refine my thoughts and theories about language models. Bill Stokoe, who spent years encouraging me via email to continue with my linguistic and computer science studies, and who I finally met in person shortly before his death. Jerry Keuper who, after hearing I was interested in computational linguistics, sent me a copy of his book on Chinese idiom as well as a few chapters of a book he was writing on Spanish idiom, and who also called me on my first day as a new doctoral student at Nova to encourage me.

Stokoe and Keuper are both missed as well. To my daughter Meagan, who is now away at college studying industrial design, for her love and support, and for as a young child being proud to tell her friends that her mother was studying for a “doctorette.” And finally last, but certainly not least, to my professors at Nova Southeastern University, all of whom supplied me with a quality education. Rollie Guild, who started working with me when I was a new student at Nova, directing my early research. Lee Leitner, who continued after Dr.

Guild’s death, helping me take a vague idea and start to turn it into a dissertation. To my dissertation committee, Dr. Michael Laszlo, Dr. James Cannady and Dr.

Amon Seagull, for their interminable patience, and for the excellent direction and feedback they provided me with while working on this dissertation. Table of Contents Abstract. iii List of Tables. x List of Figures.

4 Can Something “Not in the Real World” be Represented in a Classic Taxonomy? 4 Can There be More than One Conceptual System?. 5 Can an Interlingua Represent Concepts Independent of Language?. 9 The Autonomy Hypothesis and the Lexical Independence Hypothesis. 10 Pre- and Post-editing to Resolve Ambiguity.

Relevance, Significance, and Brief Review of the Literature. 12 Early Attempts at Machine Translation of Natural Language. 13 Is There a Language Acquisition Device?. 15 The Development of Tropes.

18 Perceptual Conceptualization and Lexicalization. 20 Basic-level Perceptual Categorization, Prototypes, and Radial Structures. 25 Perceptual Categorization in Navaho, Japanese, and ASL. 29 Morphology and Categorization.

38 Arbitrary “one criterion” Categorization and Ad-hoc Categorization. 43 Mythos-based Categorization in Dyirbal. 48 Part-whole Hierarchies Across Languages. 52 Contrastive Ambiguity and Taxonomic Categorization.

52 Taxonomic Categorization in German. 53 Category Markedness and Taxonomic Ambiguity. 64 Where’s the Syntax?. 69 The Proposed Model.

70 Paradigm and Syntagm. 74 Time Metaphor and Orientational Metaphor. 80 Tenor-vehicle Metaphor. 87 Mereologic and Taxonomic Ambiguity.

90 Mereologic and Taxonomic Synecdoche. 92 Chunking, Idiom, and Irony. 97 Grammatical Inflection in Idiom. 98 Thematic-and Function-based Metonymy.

99 Format for Presenting Results. 99 Evaluation of the Results. 106 Brain Structure Modules. 109 The Right Anterior Temporal Lobe Module.

111 Idioms and Collocations. 112 Agglutinative and Derivational Languages. 114 The Right Frontal Lobe Module. 117 vi Switching Conceptual Systems.

117 The Left Anterior Temporal Lobe Module. 118 Grammatical Inflection and Function Words. 118 The Right Posterior Temporal Lobe Module. 120 Perceptual Categorization and Perceptual Classifiers.

120 Time and Orientational Metaphor. 134 Thematic Categorization and Metonymy. 136 The Left Motor Cortex Module. 137 Functional Categorization and Contrastive Ambiguity.

138 Functional Categorization and Complementary Ambiguity. 139 Verb-Noun Pairs and Subject-Verb-Object Groupings. 145 Retention of S-V-O in Broca’s Aphasia. 146 Verb Loss in ALS.

147 The Left Posterior Temporal Lobe Module. 147 Hierarchical Categorization and Hierarchical Ambiguity. 147 Mereology-based Interlingua. 149 Mereology-based Synecdoche.

150 Taxonomy-based Synecdoche. 153 The Right Motor Cortex Module. 155 Functional Categorization and Tenor-vehicle Metaphor. 156 The Left Frontal Lobe Module.

157 The Impact of “Not Implemented”. 159 Comparison to Language Acquisition, Aphasiology, and Autism Models. 160 Comparison to Learning Models. 160 Comparison to Propositional Models.

160 Comparison to Grammatical Models. 161 Comparison to Statistical Models. 162 Comparison to Interlingual Models. 163 Comparison to Cruse’s Examples of Taxonomic Ambiguity.

163 Comparison to Pustejovsky’s Contrastive & Complementary Ambiguity. 163 Comparison to Examples of Functional Ambiguity. 164 Comparison to Examples in Fillmore’s Case Theory. 165 Comparison to Jackendoff’s Examples of Thematic-based Metonymy.

167 Comparison to Chandler’s Examples of Synecdoche. 168 Comparison to Examples of Tenor-vehicle Metaphor. 169 Comparison to Narayanan’s Examples of Metaphor. 169 Comparison to Lakoff’s Examples of Classifiers and Categorization.

172 vii Comparison to Lakoff’s Examples of Ontological Metaphor. 173 Summary of the Results. Conclusions, Implications, Recommendations, and Summary. 177 Evaluation of Error.

178 Limitations of the Findings. 189 Computer Models of Mental Processes. Some History of Computers and Natural Language. 192 Rule-based Direct Translations.

193 Corpus-based Systems—Statistical Methods and Example-based Translation. 195 Knowledge-based Systems. 197 The Triad of Impairment. 197 The Rates of Autism in Neurocutaneous Disorders.