Teaching Tale Types to a Computer: A First Experiment with the Annotated Folktales Collection

Abstract

Abstract Computational motif detection in folk narratives is an unresolved problem, partly because motifs are formally fluid, and because test collections to teach machine learning algorithms are not generally available or big enough to yield robust predictions for expert confirmation. As a result, standard tale typology based on texts as motif strings renders its computational reproduction an automatic classification exercise. In this brief communication, to report work in progress we use the Support Vector Machine algorithm on the ten best populated classes of the Annotated Folktales test collection, to predict text membership in their internationally accepted categories. The classification result was evaluated using recall, precision, and F1 scores. The F1 score was in the range 0.8–1.0 for all the selected tale types except for type 275 (The Race between Two Animals), which, although its recall rate was 1.0, suffered from a low precision.