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Cross-Document Narrative Frame Alignment

Authors Ben Miller, Ayush Shrestha, Jennifer Olive, Shakthidhar Gopavaram

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Ben Miller
Ayush Shrestha
Jennifer Olive
Shakthidhar Gopavaram

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Ben Miller, Ayush Shrestha, Jennifer Olive, and Shakthidhar Gopavaram. Cross-Document Narrative Frame Alignment. In 6th Workshop on Computational Models of Narrative (CMN 2015). Open Access Series in Informatics (OASIcs), Volume 45, pp. 124-132, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015) https://doi.org/10.4230/OASIcs.CMN.2015.124

Abstract

Automated cross-document comparison of narrative facilitates co-reference and event similarity identification in the retellings of stories from different perspectives. With attention to these outcomes, we introduce a method for the unsupervised generation and comparison of graph representations of narrative texts. Composed of the entity-entity relations that appear in the events of a narrative, these graphs are represented by adjacency matrices populated with text extracted using various natural language processing tools. Graph similarity analysis techniques are then used to measure the similarity of events and the similarity of character function between stories. Designed as an automated process, our first application of this method is against a test corpus of 10 variations of the Aarne-Thompson type 333 story, "Little Red Riding Hood." Preliminary experiments correctly co-referenced differently named entities from story variations and indicated the relative similarity of events in different iterations of the tale despite their order differences. Though promising, this work in progress also indicated some incorrect correlations between dissimilar entities.

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Keywords
  • computational narrative
  • natural language processing
  • graph theory
  • text mining

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