%0 Conference Proceedings %T On Training Classifiers for Linking Event Templates %A Piskorski, Jakub %A Šarić, Fredi %A Zavarella, Vanni %A Atkinson, Martin %Y Caselli, Tommaso %Y Miller, Ben %Y van Erp, Marieke %Y Vossen, Piek %Y Palmer, Martha %Y Hovy, Eduard %Y Mitamura, Teruko %Y Caswell, David %Y Brown, Susan W. %Y Bonial, Claire %S Proceedings of the Workshop Events and Stories in the News 2018 %D 2018 %8 August %I Association for Computational Linguistics %C Santa Fe, New Mexico, U.S.A %F piskorski-etal-2018-training %X 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. %U https://aclanthology.org/W18-4309 %P 68-78