  We extend the well-known word analogy task to a one-to-X formulation,
including one-to-none cases, when no correct answer exists. The task is cast as
a relation discovery problem and applied to historical armed conflicts
datasets, attempting to predict new relations of type `location:armed-group'
based on data about past events. As the source of semantic information, we use
diachronic word embedding models trained on English news texts. A simple
technique to improve diachronic performance in such task is demonstrated, using
a threshold based on a function of cosine distance to decrease the number of
false positives; this approach is shown to be beneficial on two different
corpora. Finally, we publish a ready-to-use test set for one-to-X analogy
evaluation on historical armed conflicts data.
