@inproceedings{halterman-2019-geolocating,
title = "Geolocating Political Events in Text",
author = "Halterman, Andrew",
editor = "Volkova, Svitlana and
Jurgens, David and
Hovy, Dirk and
Bamman, David and
Tsur, Oren",
booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2104",
doi = "10.18653/v1/W19-2104",
pages = "29--39",
abstract = "This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event{--}location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.",
}
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%0 Conference Proceedings
%T Geolocating Political Events in Text
%A Halterman, Andrew
%Y Volkova, Svitlana
%Y Jurgens, David
%Y Hovy, Dirk
%Y Bamman, David
%Y Tsur, Oren
%S Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F halterman-2019-geolocating
%X This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event–location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.
%R 10.18653/v1/W19-2104
%U https://aclanthology.org/W19-2104
%U https://doi.org/10.18653/v1/W19-2104
%P 29-39
Markdown (Informal)
[Geolocating Political Events in Text](https://aclanthology.org/W19-2104) (Halterman, NLP+CSS 2019)
ACL
- Andrew Halterman. 2019. Geolocating Political Events in Text. In Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, pages 29–39, Minneapolis, Minnesota. Association for Computational Linguistics.