Diachronic word embeddings and semantic shifts: a survey

Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski, Erik Velldal


Abstract
Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.
Anthology ID:
C18-1117
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1384–1397
URL:
https://www.aclweb.org/anthology/C18-1117
DOI:
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PDF:
https://www.aclweb.org/anthology/C18-1117.pdf