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
Editors:
Tommaso Caselli, Ben Miller, Marieke van Erp, Piek Vossen, Martha Palmer, Eduard Hovy, Teruko Mitamura, David Caswell, Susan W. Brown, Claire Bonial
Venue:
EventStory
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–78
Language:
URL:
https://aclanthology.org/W18-4309
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-4309.pdf