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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 asa relation discovery problem and applied to historical armed conflictsdatasets, attempting to predict new relations of type `location:armed-group'based on data about past events. As the source of semantic information, we usediachronic word embedding models trained on English news texts. A simpletechnique to improve diachronic performance in such task is demonstrated, usinga threshold based on a function of cosine distance to decrease the number offalse positives; this approach is shown to be beneficial on two differentcorpora. Finally, we publish a ready-to-use test set for one-to-X analogyevaluation on historical armed conflicts data.

One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts