On Training Classifiers for Linking Event Templates
Jakub Piskorski, Fredi Šarić, Vanni Zavarella, Martin Atkinson
Abstract
The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events.- Anthology ID:
- W18-4309
- Volume:
- Proceedings of the Workshop Events and Stories in the News 2018
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, U.S.A
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 68–78
- Language:
- URL:
- https://aclanthology.org/W18-4309
- DOI:
- Cite (ACL):
- Jakub Piskorski, Fredi Šarić, Vanni Zavarella, and Martin Atkinson. 2018. On Training Classifiers for Linking Event Templates. In Proceedings of the Workshop Events and Stories in the News 2018, pages 68–78, Santa Fe, New Mexico, U.S.A. Association for Computational Linguistics.
- Cite (Informal):
- On Training Classifiers for Linking Event Templates (Piskorski et al., EventStory 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-4309.pdf