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This paper deals with using word embedding models to trace the temporaldynamics of semantic relations between pairs of words. The set-up is similar tothe well-known analogies task, but expanded with a time dimension. To this end,we apply incremental updating of the models with new training texts, includingincremental vocabulary expansion, coupled with learned transformation matricesthat let us map between members of the relation. The proposed approach isevaluated on the task of predicting insurgent armed groups based ongeographical locations. The gold standard data for the time span 1994--2010 isextracted from the UCDP Armed Conflicts dataset. The results show that themethod is feasible and outperforms the baselines, but also that important workstill remains to be done.

Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants