Folktale classification using learning to rank

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    8 Citations (Scopus)
    47 Downloads (Pure)


    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 languageUndefined
    Title of host publication35th European Conference on IR Research, ECIR 2013
    Place of PublicationBerlin
    Number of pages12
    ISBN (Print)978-3-642-36972-8
    Publication statusPublished - Mar 2013
    Event35th European Conference on Information Retrieval, ECIR 2013: (IR Resarch) - Moscow, Russian Federation
    Duration: 24 Mar 201327 Mar 2013
    Conference number: 35

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference35th European Conference on Information Retrieval, ECIR 2013
    Abbreviated titleECIR
    Country/TerritoryRussian Federation


    • EWI-23552
    • METIS-297756
    • IR-87090

    Cite this