@InProceedings{piskorski-EtAl:2018:W18-43,
  author    = {Piskorski, Jakub  and  Saric, Fredi  and  Zavarella, Vanni  and  Atkinson, Martin},
  title     = {On Training Classifiers for Linking Event Templates},
  booktitle = {Proceedings of the Workshop Events and Stories in the News 2018},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, U.S.A},
  publisher = {Association for Computational Linguistics},
  pages     = {68--78},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-4309}
}