@InProceedings{kutuzov-velldal-ovrelid:2017:EMNLP2017,
  author    = {Kutuzov, Andrey  and  Velldal, Erik  and  {\O}vrelid, Lilja},
  title     = {Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1824--1829},
  abstract  = {This paper deals with using word embedding models to trace the temporal
	dynamics of semantic relations between pairs of words. The set-up is similar to
	the well-known analogies task, but expanded with a time dimension. To this end,
	we apply incremental updating of the models with new training texts, including
	incremental vocabulary expansion, coupled with learned transformation matrices
	that let us map between members of the relation.
	The proposed approach is evaluated on the task of predicting insurgent armed
	groups based on geographical locations. The gold standard data for the time
	span 1994--2010 is extracted from the UCDP Armed Conflicts dataset. The results
	show that the method is feasible and outperforms the baselines, but also that
	important work still remains to be done.},
  url       = {https://www.aclweb.org/anthology/D17-1194}
}