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arXiv · 1907.12674

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

Andrey Kutuzov·Erik Velldal·Lilja Øvrelid
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Abstract

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.

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Andrey Kutuzov, Erik Velldal, Lilja Øvrelid. 2019-07-29. One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts. https://arxiv.org/abs/1907.12674

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