Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-36973-5_17 | ECIR |
Keywords | Field | DocType |
similar story,folktale researcher,fairy tale,topical similarity,information retrieval,baseline system,story type,folktale classification,information retrieval problem,story type classification scheme,text similarity | Learning to rank,Data mining,Information retrieval,Query expansion,Computer science,Classification scheme,Partial match,Baseline system | Conference |
Citations | PageRank | References |
4 | 0.51 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Nguyen | 1 | 682 | 49.92 |
| Dolf Trieschnigg | 2 | 525 | 42.73 |
| Mariët Theune | 3 | 379 | 43.91 |