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 language | Undefined |
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Title of host publication | 35th European Conference on IR Research, ECIR 2013 |
Place of Publication | Berlin |
Publisher | Springer |
Pages | 195-206 |
Number of pages | 12 |
ISBN (Print) | 978-3-642-36972-8 |
DOIs | |
Publication status | Published - Mar 2013 |
Event | 35th European Conference on Information Retrieval, ECIR 2013: (IR Resarch) - Moscow, Russian Federation Duration: 24 Mar 2013 → 27 Mar 2013 Conference number: 35 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Verlag |
Volume | 7814 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 35th European Conference on Information Retrieval, ECIR 2013 |
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Abbreviated title | ECIR |
Country/Territory | Russian Federation |
City | Moscow |
Period | 24/03/13 → 27/03/13 |
Keywords
- EWI-23552
- METIS-297756
- IR-87090