Article | Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden | Machine Learning for Rhetorical Figure Detection: More Chiasmus with Less Annotation
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Title:
Machine Learning for Rhetorical Figure Detection: More Chiasmus with Less Annotation
Author:
Marie Dubremetz: Dept. of Linguistics and Philology, Uppsala University, Uppsala, Sweden Joakim Nivre: Dept. of Linguistics and Philology, Uppsala University, Uppsala, Sweden
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden
Issue:
131
Article no.:
005
Pages:
37-45
No. of pages:
9
Publication type:
Abstract and Fulltext
Published:
2017-05-08
ISBN:
978-91-7685-601-7
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Figurative language identification is a hard problem for computers. In this paper we handle a subproblem: chiasmus detection. By chiasmus we understand a rhetorical figure that consists in repeating two elements in reverse order: “First shall be last, last shall be first”. Chiasmus detection is a needle-in-the-haystack problem with a couple of true positives for millions of false positives. Due to a lack of annotated data, prior work on detecting chiasmus in running text has only considered hand-tuned systems. In this paper, we explore the use of machine learning on a partially annotated corpus. With only 31 positive instances and partial annotation of negative instances, we manage to build a system that improves both precision and recall compared to a hand-tuned system using the same features. Comparing the feature weights learned by the machine to those give by the human, we discover common characteristics of chiasmus.

Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Author:
Marie Dubremetz, Joakim Nivre
Title:
Machine Learning for Rhetorical Figure Detection: More Chiasmus with Less Annotation
References:

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Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Author:
Marie Dubremetz, Joakim Nivre
Title:
Machine Learning for Rhetorical Figure Detection: More Chiasmus with Less Annotation
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