Folktale Classification Using Learning to Rank

Dong Nguyen, Dolf Trieschnigg, Mariët Theune

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings
EditorsPavel Serdyukov, Pavel Braslavski, Sergei O. Kuznetsov, Jaap Kamps, Stefan Rüger, Eugene Agichtein, Ilya Segalovich, Emine Yilmaz
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Pages195-206
Number of pages12
ISBN (Electronic)978-3-642-36973-5
ISBN (Print)978-3-642-36972-8
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer Berlin Heidelberg
Volume7814
ISSN (Print)0302-9743

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