@inproceedings{piskorski-etal-2018-training,
title = "On Training Classifiers for Linking Event Templates",
author = "Piskorski, Jakub and
{\v{S}}ari{\'c}, Fredi and
Zavarella, Vanni and
Atkinson, Martin",
editor = "Caselli, Tommaso and
Miller, Ben and
van Erp, Marieke and
Vossen, Piek and
Palmer, Martha and
Hovy, Eduard and
Mitamura, Teruko and
Caswell, David and
Brown, Susan W. and
Bonial, Claire",
booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, U.S.A",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-4309",
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.",
}
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%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
Markdown (Informal)
[On Training Classifiers for Linking Event Templates](https://aclanthology.org/W18-4309) (Piskorski et al., EventStory 2018)
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.