Tracing armed conflicts with diachronic word embedding models

Andrey Kutuzov, Erik Velldal, Lilja Øvrelid


Abstract
Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the conflict research field as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting ‘cultural’ semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the ‘anchor words’ method which outperforms previous approaches on this set.
Anthology ID:
W17-2705
Volume:
Proceedings of the Events and Stories in the News Workshop
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
31–36
Language:
URL:
https://www.aclweb.org/anthology/W17-2705
DOI:
10.18653/v1/W17-2705
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2705.pdf
Poster:
 W17-2705.Poster.pdf