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
Venues:
COLING | EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–78
Language:
URL:
https://www.aclweb.org/anthology/W18-4309
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-4309.pdf