@inproceedings{kutuzov-etal-2019-one,
    title = "One-to-{X} Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts",
    author = "Kutuzov, Andrey  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    editor = "Tahmasebi, Nina  and
      Borin, Lars  and
      Jatowt, Adam  and
      Xu, Yang",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-4724/",
    doi = "10.18653/v1/W19-4724",
    pages = "196--201",
    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 {\textquoteleft}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."
}