One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts

Andrey Kutuzov, Erik Velldal, Lilja Øvrelid


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.
Anthology ID:
W19-4724
Volume:
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
196–201
Language:
URL:
https://www.aclweb.org/anthology/W19-4724
DOI:
10.18653/v1/W19-4724
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-4724.pdf