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Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
This paper deals with using word embedding models to trace the temporal
dynamics of semantic relations between pairs of words. The set-up is similar to
the well-known analogies task, but expanded with a time dimension. To this end,
we apply incremental updating of the models with new training texts, including
incremental vocabulary expansion, coupled with learned transformation matrices
that let us map between members of the relation. The proposed approach is
evaluated on the task of predicting insurgent armed groups based on
geographical locations. The gold standard data for the time span 1994--2010 is
extracted from the UCDP Armed Conflicts dataset. The results show that the
method is feasible and outperforms the baselines, but also that important work
still remains to be done.Comment: to appear in EMNLP 2017 proceeding
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