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23552 Folktale classification using learning to rank
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Nguyen, Dong-Phuong and Trieschnigg, R.B. and Theune, M. (2013) Folktale classification using learning to rank. In: 35th European Conference on IR Research, ECIR 2013, 24-27 Mar 2013, Moscow, Russia. pp. 195-206. Lecture Notes in Computer Science 7814. Springer Verlag. ISSN 0302-9743 ISBN 978-3-642-36972-8

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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.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-HMI: Human Media Interaction
Research Program:CTIT-NICE: Natural Interaction in Computer-mediated Environments
Research Project:FACT: Folktales As Classifiable Texts
ID Code:23552
Deposited On:09 September 2013
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