000 04297nam a22005175i 4500
001 978-3-031-02147-3
003 DE-He213
005 20240730163823.0
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008 220601s2013 sz | s |||| 0|eng d
020 _a9783031021473
_9978-3-031-02147-3
024 7 _a10.1007/978-3-031-02147-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aMani, Inderjeet.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980658
245 1 0 _aComputational Modeling of Narrative
_h[electronic resource] /
_cby Inderjeet Mani.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXVII, 124 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aList of Figures -- List of Tables -- Narratological Background -- Characters as Intentional Agents -- Time -- Plot -- Summary and Future Directions.
520 _aThe 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. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_980659
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010194
776 0 8 _iPrinted edition:
_z9783031032752
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980660
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02147-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c85007
_d85007