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.- Anthology ID:
- D17-1194
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1824–1829
- Language:
- URL:
- https://aclanthology.org/D17-1194
- DOI:
- 10.18653/v1/D17-1194
- Cite (ACL):
- Andrey Kutuzov, Erik Velldal, and Lilja Øvrelid. 2017. Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1824–1829, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants (Kutuzov et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/D17-1194.pdf