Folktale classification using learning to rank

Dong-Phuong Nguyen; Rudolf Berend Trieschnigg; Mariet Theune

doi:10.1007/978-3-642-36973-5_17

Folktale classification using learning to rank

Dong-Phuong Nguyen, Rudolf Berend Trieschnigg, Mariet Theune

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

37 Downloads (Pure)

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
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	https://doi.org/10.1007/978-3-642-36973-5_17
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
Publisher	Springer Verlag
Volume	7814
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	35th European Conference on Information Retrieval, ECIR 2013
Abbreviated title	ECIR
Country	Russian Federation
City	Moscow
Period	24/03/13 → 27/03/13

Keywords

EWI-23552
METIS-297756
IR-87090

Access to Document

10.1007/978-3-642-36973-5_17

nguyen-ecir2013
open

Cite this

@inproceedings{2f088336aff0466a836ee480e0d1f448,

title = "Folktale classification using learning to rank",

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.",

keywords = "EWI-23552, METIS-297756, IR-87090",

author = "Dong-Phuong Nguyen and Trieschnigg, {Rudolf Berend} and Mariet Theune",

note = "10.1007/978-3-642-36973-5_17 ; null ; Conference date: 24-03-2013 Through 27-03-2013",

year = "2013",

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language = "Undefined",

isbn = "978-3-642-36972-8",

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Nguyen, D-P, Trieschnigg, RB & Theune, M 2013, Folktale classification using learning to rank. in 35th European Conference on IR Research, ECIR 2013. Lecture Notes in Computer Science, vol. 7814, Springer, Berlin, pp. 195-206, 35th European Conference on Information Retrieval, ECIR 2013, Moscow, Russian Federation, 24/03/13. https://doi.org/10.1007/978-3-642-36973-5_17

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T1 - Folktale classification using learning to rank

AU - Nguyen, Dong-Phuong

AU - Trieschnigg, Rudolf Berend

AU - Theune, Mariet

N1 - Conference code: 35

PY - 2013/3

Y1 - 2013/3

N2 - 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.

AB - 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.

KW - EWI-23552

KW - METIS-297756

KW - IR-87090

U2 - 10.1007/978-3-642-36973-5_17

DO - 10.1007/978-3-642-36973-5_17

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