Folktale classification using learning to rank

Authors:
Dong Nguyen;Dolf Trieschnigg;Mariët Theune
Affiliations:
Human Media Interaction, University of Twente, Enschede, The Netherlands;Human Media Interaction, University of Twente, Enschede, The Netherlands;Human Media Interaction, University of Twente, Enschede, The Netherlands
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

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Abstract

We present a learning to rank approach to classify folktales, such as fairy tales and urban legends, according to their story type, a concept that is widely used by folktale researchers to organize and classify folktales. A story type represents a collection of similar stories often with recurring plot and themes. Our work is guided by two frequently used story type classification schemes. Contrary to most information retrieval problems, the text similarity in this problem goes beyond topical similarity. We experiment with approaches inspired by distributed information retrieval and features that compare subject-verb-object triplets. Our system was found to be highly effective compared with a baseline system.