Record:   Prev Next
Author Mani, Inderjeet
Title Computational modeling of narrative [electronic resource] / Inderjeet Mani
Imprint San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2013
book jacket
Descript 1 electronic text (xvii, 124 p.) : ill., digital file
Series Synthesis lectures on human language technologies, 1947-4059 ; # 18
Synthesis digital library of engineering and computer science
Synthesis lectures on human language technologies ; # 18. 1947-4059
Note Part of: Synthesis digital library of engineering and computer science
Series from website
Includes bibliographical references (p. 103-115) and index
List of figures -- List of tables -- Preface --
1. Narratological background -- 1.1 Introduction -- 1.2 Narrator characteristics -- 1.2.1 Narrator identity -- 1.2.2 Narrative distance -- 1.2.3 Narrator perspective -- 1.3 Narrative levels -- 1.3.1 Embedded narratives -- 1.3.2 Narrative threads -- 1.3.3 Subordinated discourse -- 1.4 Time -- 1.4.1 Background -- 1.4.2 Narrative time -- 1.4.3 Narrative order -- 1.5 Audience -- 1.5.1 Preliminaries -- 1.5.2 Audience response -- 1.6 Fabula -- 1.6.1 Introduction -- 1.6.2 Basic ontology -- 1.6.3 Worlds and accessibility -- 1.7 NarrativeML --
2. Characters as intentional agents -- 2.1 Introduction -- 2.1.1 Preliminaries -- 2.1.2 Plan recognition -- 2.1.3 Plan synthesis -- 2.2 Scripts and case-based reasoning -- 2.3 Goals in plan synthesis -- 2.3.1 Character goals -- 2.3.2 Narrative goals -- 2.3.3 Incorporating coarse-grained goal structure -- 2.4 Planning for interactive narrative -- 2.4.1 Preliminaries -- 2.4.2 Replanning -- 2.4.3 Generating preferred outcomes -- 2.5 Evaluating event outcomes -- 2.6 Narratological implications -- 2.7 NarrativeML, redux -- 2.8 Discussion --
3. Time -- 3.1 Introduction -- 3.2 Temporal representation -- 3.3 Annotation scheme -- 3.4 Narratological implications -- 3.5 Automatic approaches -- 3.5.1 Natural language generation -- 3.5.2 Time tagging -- 3.5.3 Event tagging -- 3.5.4 Inferring temporal relations -- 3.6 NarrativeML, revisited -- 3.7 Conclusion --
4. Plot -- 4.1 Introduction -- 4.1.1 Background -- 4.1.2 Aristotelian plot -- 4.1.3 Narrative arc -- 4.1.4 Heroic quests -- 4.2 Narrative functions -- 4.3 Story grammars -- 4.4 Causal models of plot -- 4.4.1 Plot units -- 4.4.2 Bremond's approach -- 4.4.3 Doxastic preferences -- 4.4.4 Story intention graphs -- 4.5 Narrative event chain summaries -- 4.6 Comparison of plot models -- 4.7 Narratological implications -- 4.8 NarrativeML -- 4.9 Conclusion --
5. Summary and future directions -- 5.1 Summary -- 5.1.1 Chapter summaries -- 5.1.2 NarrativeML -- 5.1.3 Narratological reflections -- 5.2 Future directions --
Bibliography -- Author's biography -- Index
Abstract freely available; full-text restricted to subscribers or individual document purchasers
Compendex
INSPEC
Google scholar
Google book search
Mode of access: World Wide Web
System requirements: Adobe Acrobat Reader
The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists
Also available in print
Title from PDF t.p. (viewed on January 18, 2013)
Morgan & Claypool
Link Print version: 9781608459810
Subject Narration (Rhetoric) -- Mathematical models
Computational linguistics
narrative modeling
digital storytelling
computational narrative
story understanding,
story generation
interactive fiction
character modeling
planning
case-based reasoning
temporal reasoning
plot
narratology
Record:   Prev Next