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

arXiv: https://arxiv.org/abs/1907.12674 · HTML: https://mlsanity.com/paper/1907.12674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Predicting drug-gene relations via analogy tasks with word embeddings](https://mlsanity.com/paper/2406.00984/) (2024)
- [From cart to truck: meaning shift through words in English in the last two centuries](https://mlsanity.com/paper/2408.16209/) (2024)

## Abstract (arXiv)

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.