An NLP-based cross-document approach to narrative structure discovery.
Structural similarities across narratives play an important role in many areas of humanities research. In this article, we describe a methodology and an implementation to uncover such similarities automatically in two application scenarios. In both scenarios—ritual and folktale studies—existing rese...
| Publicado en: | Literary & Linguistic Computing Vol. 29; no. 4; pp. 583 - 606 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Dec2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=99377363&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 99377363 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: Dec2014 vid: 29 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 99377363 10.1093/llc/fqu055 ppf: 583 ppct: 23 formats: fmt: @attributes: type: P size: 12.4MB tig: atl: An NLP-based cross-document approach to narrative structure discovery. aug: au: Reiter, Nils Frank, Anette Hellwig, Oliver affil: Heidelberg University, Germany su: Computational linguistics Narratives Tale (Literary form) Narrative discourse analysis Humanities research sug: subj: Computational linguistics Narratives Tale (Literary form) Narrative discourse analysis Humanities research ab: Structural similarities across narratives play an important role in many areas of humanities research. In this article, we describe a methodology and an implementation to uncover such similarities automatically in two application scenarios. In both scenarios—ritual and folktale studies—existing research examines similarities of narratives on a structural level and discusses structural principles that govern the combination of individual events to tales or rituals. We present a largely unsupervised and fully automated alignment-based approach for the detection of structural similarities of narratives that allows for data-driven quantitative studies of narrative structure. Our approach makes use of an adaptable, computational linguistic processing architecture that creates integrated discourse representations of events, participants, and their relations. Our contributions are twofold and crucially build on the automatically constructed discourse representations: (I) We examine different 'semantics-driven cross-document alignment' algorithms that determine (sequences of) events shared between narratives, to support the search for recurrent elements in their structure. The alignment algorithms are evaluated in two experiments. (2) We develop 'tools for exploration and interpretation' that we offer to humanities researchers for investigation of the analyzed data. These include search facilities, visualizations, statistical overviews, and a graph-based algorithm that identifies densely aligned regions across documents for targeted inspection. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Literary & Linguistic Computing holder: Oxford University Press / USA dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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