Abstract
Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the conflict research field as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting ‘cultural’ semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the ‘anchor words’ method which outperforms previous approaches on this set.- Anthology ID:
- W17-2705
- Volume:
- Proceedings of the Events and Stories in the News Workshop
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Tommaso Caselli, Ben Miller, Marieke van Erp, Piek Vossen, Martha Palmer, Eduard Hovy, Teruko Mitamura, David Caswell
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–36
- Language:
- URL:
- https://aclanthology.org/W17-2705
- DOI:
- 10.18653/v1/W17-2705
- Cite (ACL):
- Andrey Kutuzov, Erik Velldal, and Lilja Øvrelid. 2017. Tracing armed conflicts with diachronic word embedding models. In Proceedings of the Events and Stories in the News Workshop, pages 31–36, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Tracing armed conflicts with diachronic word embedding models (Kutuzov et al., EventStory 2017)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/W17-2705.pdf