German in Flux: Detecting Metaphoric Change via Word Entropy
This work addresses the challenge of tracking semantic shifts, specifically metaphoric change, for linguists and NLP researchers, though it is incremental as it transfers existing ideas to a new domain.
The paper tackles the problem of detecting metaphoric change in language by using an information-theoretic entropy measure, achieving high performance with an unsupervised and language-independent model. It also introduces the first diachronic test set for German to standardize annotation for metaphoric change.
This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We also build the first diachronic test set for German as a standard for metaphoric change annotation. Our model shows high performance, is unsupervised, language-independent and generalizable to other processes of semantic change.