EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference
Doi
Abstract
Word embeddings are increasingly used for
the automatic detection of semantic change;
yet, a robust evaluation and systematic comparison
of the choices involved has been lacking.
We propose a new evaluation framework
for semantic change detection and find that (i)
using the whole time series is preferable over
only comparing between the first and last time
points; (ii) independently trained and aligned
embeddings perform better than continuously
trained embeddings for long time periods; and
(iii) that the reference point for comparison
matters. We also present an analysis of the
changes detected on a large Twitter dataset
spanning 5.5 years.This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1