Linking Motif Sequences with Tale Types by Machine Learning

Authors Nir Ofek, Sándor Darányi, Lior Rokach

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Nir Ofek
Sándor Darányi
Lior Rokach

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Nir Ofek, Sándor Darányi, and Lior Rokach. Linking Motif Sequences with Tale Types by Machine Learning. In 2013 Workshop on Computational Models of Narrative. Open Access Series in Informatics (OASIcs), Volume 32, pp. 166-182, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)


Abstract units of narrative content called motifs constitute sequences, also known as tale types. However whereas the dependency of tale types on the constituent motifs is clear, the strength of their bond has not been measured this far. Based on the observation that differences between such motif sequences are reminiscent of nucleotide and chromosome mutations in genetics, i.e., constitute "narrative DNA", we used sequence mining methods from bioinformatics to learn more about the nature of tale types as a corpus. 94% of the Aarne-Thompson-Uther catalogue (2249 tale types in 7050 variants) was listed as individual motif strings based on the Thompson Motif Index, and scanned for similar subsequences. Next, using machine learning algorithms, we built and evaluated a classifier which predicts the tale type of a new motif sequence. Our findings indicate that, due to the size of the available samples, the classification model was best able to predict magic tales, novelles and jokes.
  • Narrative DNA
  • tale types
  • motifs
  • type-motif correlation
  • machine learning


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