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Modeling Narrative Discourse David K. Elson

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Modeling Narrative Discourse David K. Elson Modeling Narrative Discourse David K. Elson Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2012 c 2012 David K. Elson All Rights Reserved ABSTRACT Modeling Narrative Discourse David K. Elson This thesis describes new approaches to the formal modeling of narrative discourse. Al- though narratives of all kinds are ubiquitous in daily life, contemporary text processing techniques typically do not leverage the aspects that separate narrative from expository discourse. We describe two approaches to the problem. The first approach considers the conversational networks to be found in literary fiction as a key aspect of discourse coher- ence; by isolating and analyzing these networks, we are able to comment on longstanding literary theories. The second approach proposes a new set of discourse relations that are specific to narrative. By focusing on certain key aspects, such as agentive characters, goals, plans, beliefs, and time, these relations represent a theory-of-mind interpretation of a text. We show that these discourse relations are expressive, formal, robust, and through the use of a software system, amenable to corpus collection projects through the use of trained annotators. We have procured and released a collection of over 100 encodings, covering a set of fables as well as longer texts including literary fiction and epic poetry. We are able to inferentially find similarities and analogies between encoded stories based on the proposed relations, and an evaluation of this technique shows that human raters prefer such a measure of similarity to a more traditional one based on the semantic distances between story propositions. Table of Contents 1 Introduction 1 2 Literary Social Networks 10 2.1 Related Work ................................... 12 2.2 Hypotheses .................................... 13 2.3 Overview of Corpora and Methodology ..................... 15 2.4 Character Identification ............................. 20 2.5 Quoted Speech Attribution ........................... 25 2.5.1 Related Work ............................... 26 2.5.2 Methodology ............................... 26 2.5.3 Encoding, cleaning, and normalizing .................. 27 2.5.4 Dialogue chains .............................. 28 2.5.5 Syntactic categories ........................... 29 2.5.6 Feature extraction and learning ..................... 31 2.5.7 Results and discussion .......................... 33 2.6 Conversational Network Construction ..................... 34 2.7 Data Analysis ................................... 37 2.7.1 Results .................................. 38 2.7.2 Literary Interpretation of Results .................... 41 2.8 Conclusion .................................... 42 3 Story Intention Graphs 43 3.1 Goals For A New Representation ........................ 44 i 3.2 A Brief History of Narrative Modeling ..................... 50 3.2.1 Foundations in Cognitive Psychology .................. 50 3.2.2 Discourse and Literary Theory ..................... 59 3.2.3 Implemented Understanding: Scripts, Plans and Plot Units ..... 70 3.2.4 Conclusion ................................ 79 3.3 Story Intention Graphs .............................. 80 3.3.1 Textual and Timeline Layers ...................... 83 3.3.2 Interpretative Layer ........................... 96 3.3.3 Summary and Comparison to Prior Work ............... 123 3.4 Conclusion .................................... 125 4 Scheherazade 128 4.1 Data Structure and Architecture ........................ 129 4.2 Semantic Network Engine and Story Logic Manager ............. 133 4.3 Graphical Annotation Interface ......................... 142 4.3.1 Related work ............................... 144 4.3.2 Overview of annotation procedure ................... 147 4.3.3 Object and theme extraction ...................... 150 4.3.4 Propositional modeling ......................... 155 4.3.5 Interpretative panel ........................... 162 4.3.6 Conclusion ................................ 167 4.4 Text Generation: Assigning Tense and Aspect ................. 167 4.4.1 Basic Planner and Realizer ....................... 168 4.4.2 Related Work ............................... 173 4.4.3 Temporal knowledge ........................... 174 4.4.4 Expressing single events from a reference state ............ 176 4.4.5 Expressing single events from a reference interval ........... 179 4.4.6 Expressing multiple events in alternate timelines ........... 181 4.4.7 Discussion ................................. 186 4.5 Conclusion .................................... 187 ii 5 Collections and Experiments 188 5.1 Corpus Collection ................................. 190 5.2 Propositional and Temporal Overlap ...................... 199 5.2.1 Paraphrase and Alignment Algorithms ................. 203 5.2.2 Evaluation ................................ 209 5.2.3 Corpus Analysis ............................. 212 5.3 Interpretative Similarities and Analogies .................... 216 5.3.1 Static Pattern Matching ......................... 218 5.3.2 Dynamic Analogy Detection ....................... 224 5.4 Evaluation ..................................... 233 5.5 Conclusions .................................... 240 6 Conclusions 243 6.1 Summary of Findings .............................. 244 6.2 Limitations and Future Work .......................... 250 6.2.1 Literary Social Networks ......................... 250 6.2.2 Story Intention Graphs ......................... 253 6.3 Contributions and General Conclusions .................... 258 A Additional Sample Visualizations 261 B Expressibility of SIGs 267 B.1 Affectual Status Transitions ........................... 270 B.2 Single-Agent Goals, Plans and Attempts .................... 276 B.3 Single-Agent Goal Outcomes and Beliefs .................... 279 B.4 Beliefs, Expectations and Dilemmas ...................... 285 B.5 Multiple-Agent Interactions ........................... 290 B.5.1 Persuasion and Deception ........................ 293 B.5.2 Complex Two-Agent Interactions .................... 296 B.6 Textual Devices .................................. 300 B.6.1 Mystery .................................. 302 iii B.6.2 Selective Inclusion and Point of View .................. 305 C SIG Closure Rules and Pattern Definitions 308 C.1 Closure Rules ................................... 308 C.2 Causality ..................................... 318 C.3 SIG Pattern Definitions ............................. 319 D Selected Aesop Fables 339 Bibliography 345 iv List of Figures 1.1 Extraction requirements for tasks in automatic narrative analysis. ..... 7 2.1 Automatically extracted conversation network for Jane Austen’s Mansfield Park. ........................................ 36 2.2 The average degree for each character as a function of the novel’s setting and its perspective. .................................. 39 2.3 Conversational networks for first-person novels like Collins’s The Woman in White are less connected due to the structure imposed by the perspective. 40 3.1 The General Recursive Transition Network, redrawn from van den Broek [1988]. ....................................... 56 3.2 Outline of “The Wily Lion” in a causal-network representation. ....... 57 3.3 Story-grammar parse of “The Wily Lion”. ................... 62 3.4 Simple plot units, redrawn from Lehnert [1981]. ................ 76 3.5 “The Wily Lion” in a plot-unit representation. ................ 77 3.6 Fragment of a SIG encoding showing textual-layer nodes, as well as Propo- sition nodes in the timeline layer. A non-contiguous subset of “The Wily Lion” is encoded. ................................. 84 3.7 Example SIG encoding (textual and timeline layers only) for a non-contiguous subset of “The Wily Lion”. ........................... 88 3.8 Telling time vs. story time. Clockwise from bottom left: a “slow” story, a “fast” story, a flashback, and “The Wily Lion” as modeled in Table 3.4. .. 90 3.9 Two configurations of alternate timelines in the timeline layer of a SIG. .. 93 v 3.10 Nested agency frames, in two forms of graphical notation. .......... 97 3.11 SIG encoding fragment showing timeline and interpretative layers, as well as the actualization status of an interpretative goal at three discrete time states. 101 3.12 SIG encoding fragment showing a possible interpretative-layer encoding for three timeline propositions in “The Wily Lion”. ................ 105 3.13 SIG encoding fragment showing a multi-step plan in “The Wily Lion”. ... 107 3.14 Causality in the SIG: The four graphical relationships between two inter- pretative propositions, A and B, from which we infer from the SIG that A (or its prevention/cessation) causes B (or its prevention/cessation). See also Appendix C.2. .................................. 110 3.15 Belief frames in a SIG can refer to (i) an agent’s belief in a proposition such as a stative, (ii) an agent’s belief in the hypothetical relationship between two propositions, or (iii) the combination of (i) and (ii) with respect to a single proposition. ................................ 113 3.16 Legal SIG encoding (top) and one that violates Affect node usage. ..... 117 3.17 Encoding showing a multi-step plan with Affect nodes in “The Wily Lion”. 121 3.18 Overall encoding for “The Wily Lion” (textual layer shown in Table 3.4). 122 3.19 SIG encoding of Forster’s distinction between a non-story (top) and a story. 126 4.1 Three classes of data are distinguished by Scheherazade, each of which applies the one
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