Daniel Hole
2017
German in Flux: Detecting Metaphoric Change via Word Entropy
Dominik Schlechtweg
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Stefanie Eckmann
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Enrico Santus
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Sabine Schulte im Walde
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Daniel Hole
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
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.
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