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Landing Propp in Interaction Space: First Steps Toward Scalable Open Domain Narrative Analysis With Predication-based Semantic Indexing
University of Texas (Austin).
University of Borås, Faculty of Librarianship, Information, Education and IT. (DIGRES)
Microsoft Bing.
University of Texas (Austin).
2015 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

In this paper, we explore the possibility of applying high-dimensionalvector representations of concept-relation-concept triplets, which have been successfullyapplied to model a small set of relationship types in the biomedicaldomain, to the task of modeling folk tales. In doing so, our ultimate aim is todevelop representations of narratives through which their underlying structurecan be compared. The current paper describes our progress toward this aim, withemphasis on addressing the technical challenges involved in moving from therelatively constrained set of relations that have been extracted from biomedicaltext to the much larger set of unnormalized relations that have been extractedfrom the open domain. A toy example using graded vectors demonstrates that ourapproach will be feasible once more material will be added to the test collection.

Place, publisher, year, edition, pages
2015.
Keywords [en]
folktales, narrative analysis, formal models, Propp, predication based semantic indexing, semantic space, distributional semantics, Vector Symbolic Architectures
National Category
Ethnology Other Computer and Information Science
Research subject
Library and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-8531DOI: 10.13140/RG.2.1.1893.6488OAI: oai:DiVA.org:hb-8531DiVA, id: diva2:894322
Conference
9th International Conference on Quantum Interaction
Available from: 2016-01-14 Created: 2016-01-14 Last updated: 2018-01-10Bibliographically approved

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Darányi, Sándor

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf