On Training Classifiers for Linking Event Templates
Jakub Piskorski | Fredi Šarić | Vanni Zavarella | Martin Atkinson
Proceedings of the Workshop Events and Stories in the News 2018
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.