Linking Motif Sequences to Tale Types by Machine Learning
2013 (English)Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
OASIcs , 2013.
, OASIcs, Vol. 32.
folk narratives, computational modelling, motifs, folktales, digital libraries
Ethnology Computer and Information Science Bioinformatics (Computational Biology)
Research subject Library and Information Science
IdentifiersURN: urn:nbn:se:hb:diva-7137Local ID: 2320/13200ISBN: 978-3-939897-57-6OAI: oai:DiVA.org:hb-7137DiVA: diva2:887844
2013 Workshop on Computational Models of Narrative