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A Computational Model for Automated Extraction of Structural Schemas from Simple Narrative Plots

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A Computational Model for Automated Extraction of Structural Schemas from Simple Narrative Plots UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE INFORMÁTICA Departamento de Software e Inteligencia Artificial- Lenguajes y Sistemas Informáticos A COMPUTATIONAL MODEL FOR AUTOMATED EXTRACTION OF STRUCTURAL SCHEMAS FROM SIMPLE NARRATIVE PLOTS. MEMORIA PARA OPTAR AL GRADO DE DOCTOR PRESENTADA POR Carlos León Aznar Bajo la dirección del doctor Pablo Gervás Gómez-Navarro Madrid, 2011 ISBN: 978-84-694-2084-3 © Carlos León Aznar, 2010 Un modelo computacional para la extracción automática de esquemas estructurales de tramas narrativas simples Tesis doctoral Presentada por D. Carlos León Aznar Dirigida por Prof. Dr. D. Pablo Gervás Gómez-Navarro Facultad de Informática Universidad Complutense de Madrid Madrid, 2010 A Computational Model for Automated Extraction of Structural Schemas from Simple Narrative Plots Ph. D. Thesis by D. Carlos León Aznar Supervised by Prof. Dr. D. Pablo Gervás Gómez-Navarro Facultad de Informática Universidad Complutense de Madrid Madrid, 2010 Thanks would like to thank my supervisor, Dr. Pablo Gervás, for everything I I have learnt from him and for all his support. This work would not have been possible without him. Thanks as well to Dr. Mark Riedl, who received me in Los Angeles at the Institute for Creative Technologies, where I learnt so much. Thanks to the NLG group at Aberdeen University, specially to Dr. Graeme Ritchie, who revised my work and gave absolutely valuable advise for it. This thesis would have not been the same without his influence. The fruitful collaboration with the NIL group has also been crucially positive during the development of the ideas for this work. Also, the scien- tific relationship emerging in the Department of Software Engineering and Artificial Intelligence (DISIA) strongly improves every research developed in it. I feel really thankful towards both groups. Thanks to the reviewers, Dr. Albert Gatt, Dr. Amílcar Cardoso and Dr. Daniel Mozos, the final version of the thesis has been greatly improved. I would also like to thank my parents and friends for all the support I have received during this period. 5 6 Abstract Building computational systems capable of creating and interpreting narrative content has been an objective of Artificial Intelligence since its beginnings. Its development, however, has been blocked by what is com- monly known as the knowledge acquisition bottleneck, which does not per- mit story processing in the large. In this dissertation, a computational system that tries to take one step forward in the target of making it possible to process narrative content on a larger scale is presented. Two stages of research towards this goal are detailed. A semantic approach to narrative processing not yielding satisfying results is first explained. Then, a different system modelling a focus shifting towards a structural management that provided better results is shown. The current state of the art is studied in detail. Empirical validation has been carried out to prove to the possible extent the proposed hypothesis (the plausibility of structural processing for narrative content). Discussion about the most important aspects and design decisions is also included, and possible future lines of investigation are also exposed. 7 8 Contents I A Computational Model for Automated Extraction of Structural Schemas from Simple Narrative Plots 25 1. Introduction 27 1.1. Motivation of the Research . 29 1.2. Objectives . 30 1.3. Hypothesis . 32 1.4. Methodology . 33 1.5. Structure . 34 1.6. Chapter Summary . 36 2. Previous Work 37 2.1. Narratology . 37 2.1.1. Fable and Discourse . 38 2.1.2. Characters . 40 2.1.3. Plot . 40 2.2. Computational Approaches to Narrative . 41 2.3. Story Generation Systems . 42 2.3.1. Novel Writer . 42 2.3.2. Grammars: Rummelhart and Joseph . 42 2.3.3. TaleSpin . 43 2.3.4. Author . 44 2.3.5. Universe . 45 2.3.6. MINSTREL . 46 2.3.7. Brutus . 48 2.3.8. MEXICA . 48 2.3.9. The Virtual Story Teller . 50 2.3.10.FABULIST . 51 2.3.11.ProtoPropp . 51 2.3.12.Summary . 53 2.4. Evaluation of Narrative . 53 2.5. Acquisition of Narrative Schemas . 55 9 10 CONTENTS 2.6. Chapter Summary . 56 3. Definition of Narrations 57 3.1. Definition of Narration Regarding Conceptual Models for this Research . 58 3.2. Formal Definition of Narration Used in this Research . 59 3.3. Constituents of Formal Narrations . 60 3.3.1. Tokens . 60 3.3.2. Variables . 60 3.3.3. Actions . 61 3.4. Narrations . 62 3.5. Space of Stories . 63 3.5.1. Size of the Space of Stories . 64 3.5.2. Subspaces of Good and Bad Narrations . 65 3.6. Chapter Summary . 66 4. Generation Based on Semantic Knowledge 67 4.1. Generation based on Evaluation . 68 4.2. Evaluation Function . 68 4.2.1. Implementation of the Evaluation Function . 71 4.3. Validation through Human Judgement . 72 4.4. Exhaustive Story Generation . 77 4.4.1. Results of the Exhaustive Exploration Approach . 78 4.5. Improving the Generation Space . 78 4.5.1. Adapting the Evaluation Function for Use as a Pruning Function . 79 4.5.2. Results of the Constrained Conceptual Space Explo- ration Approach . 80 4.6. Benefits and Drawbacks . 81 4.6.1. Influential Variables . 82 4.7. Chapter Summary . 83 5. Structural Processing of Stories 85 5.1. From a Cognitive Model to a Structural Definition . 86 5.2. Structural Properties as Structural Coherence . 87 5.2.1. Focus . 90 5.2.2. Full Connection . 90 5.2.3. Unique linkage . 91 5.3. Preconditional Links . 91 5.4. Other Properties of Preconditional Links . 93 5.5. Preconditional Chains and Preconditional Networks . 96 CONTENTS 11 5.6. Formal Definition of Structural Patterns . 96 5.6.1. Focus . 96 5.6.2. Full Connection . 97 5.6.3. Unique linkage . 97 5.7. Preconditional Rules . 98 5.8. Computing Preconditional Links in a Story . 98 5.9. Chapter Summary . 100 6. Extracting Rules 101 6.1. Set of Possible Preconditional Rules . 102 6.1.1. Size of the Set of Rules . 103 6.2. Constrained Set of Preconditional Rules . 105 6.2.1. Restrict the Search for Candidates in Stories . 105 6.2.2. Limiting the Candidates by Distance . 107 6.3. Rule-Extraction Algorithm . 108 6.3.1. Input Corpus of Simple Stories . 108 6.3.2. Generating Preconditional Rules . 109 6.3.3. Story Generation . 114 6.3.4. Gathering Human Criteria . 115 6.3.5. Gathering and Merging “Good” and “Bad” Precondi- tional Rules . 115 6.3.6. Stop Condition . 117 6.4. Chapter Summary . 117 7. Implementation and Results 119 7.1. Corpora of Stories . 119 7.1.1. Murder Stories . 120 7.1.2. Aesop’s Fables . 120 7.1.3. Operas . 124 7.2. Implementation . 125 7.2.1. Implementation of the Computation of Preconditional Links . 126 7.2.2. Implementation of the Preconditional Rules Extraction Algorithm . 126 7.2.3. Optimization of the Implementation . 126 7.3. Simple Story Generation . 127 7.3.1. Rule Application . 130 7.3.2. Simple Surface Realization . 131 7.4. Adjusting Parameters . 133 7.5. Execution Example . 134 7.5.1. Saturation . 143 12 CONTENTS 7.6. Overall Results . 144 7.7. Chapter Summary . 145 8. Discussion 147 8.1. Conceptual Aspects of the Current Approach . 147 8.1.1. Number of Used Stories in the Rule-Extraction Process 148 8.1.2. Impact of the Input Corpus in the Final Results . 148 8.1.3. Influence of Aspects Different from Coherence in Clas- sification . 149 8.1.4. Influence of Human Opinion in the Preconditional Rule Extraction Process . 150 8.1.5. Good and Bad Stories . 150 8.1.6. Language for Text Realization . 151 8.2. Comparison Against Other Approaches . 152 8.2.1. Classic Machine Learning and Rule-Gathering Algorithm152 8.3. Discussion related to the Implementation . 153 8.3.1. Appropriate Number of Input Stories for the Corpus . 154 8.3.2. Application of the Structural Information Extraction Algorithm to Other Domains . 154 8.4. Chapter Summary . 155 9. Conclusions and Future Work 157 9.1. Summary . 157 9.1.1. Scientific Contributions . 158 9.2. Conclusions of this Research . 158 9.3. Benefits and Drawbacks . 159 9.4. Future Work . 160 9.4.1. Improving the Model for Structural Definition and Pro- cessing of Narrations . 160 9.4.2. Improved Story Generation . 161 9.4.3. Improving the Implementation . 162 9.5. Applications of the Structural Processing of Narratives . 163 9.6. Chapter Summary . 164 II Resumen de la tesis en español 165 10.Introducción 167 10.1.Motivación de la investigación . 168 10.2.Objetivos . 168 10.3.Hipótesis . 169 10.4.Metodología . 169 CONTENTS 13 11.Trabajo previo 171 11.1.Narratología . ..
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