German in Flux: Detecting Metaphoric Change via Word Entropy

Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus, Sabine Schulte im Walde, Daniel Hole


Abstract
This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We build the first diachronic test set for German as a standard for metaphoric change annotation. Our model is unsupervised, language-independent and generalizable to other processes of semantic change.
Anthology ID:
K17-1036
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Roger Levy, Lucia Specia
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
354–367
Language:
URL:
https://aclanthology.org/K17-1036
DOI:
10.18653/v1/K17-1036
Bibkey:
Cite (ACL):
Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus, Sabine Schulte im Walde, and Daniel Hole. 2017. German in Flux: Detecting Metaphoric Change via Word Entropy. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 354–367, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
German in Flux: Detecting Metaphoric Change via Word Entropy (Schlechtweg et al., CoNLL 2017)
Copy Citation:
PDF:
https://aclanthology.org/K17-1036.pdf
Presentation:
 K17-1036.Presentation.pdf
Code
 Garrafao/MetaphoricChange