@inproceedings{cartier-tanev-2024-event,
title = "Event Detection in the Socio Political Domain",
author = "Cartier, Emmanuel and
Tanev, Hristo",
editor = "Afli, Haithem and
Bouamor, Houda and
Casagran, Cristina Blasi and
Ghannay, Sahar",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.politicalnlp-1.2",
pages = "12--21",
abstract = "In this paper we present two approaches for detection of socio political events: the first is based on manually crafted keyword combinations and the second one is based on a BERT classifier. We compare the performance of the two systems on a dataset of socio-political events. Interestingly, the systems demonstrate complementary performance: both showing their best accuracy on non overlapping sets of event types. In the evaluation section we provide insights on the effect of taxonomy mapping on the event detection evaluation. We also review in the related work section the most important resources and approaches for event extraction in the recent years.",
}
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%0 Conference Proceedings
%T Event Detection in the Socio Political Domain
%A Cartier, Emmanuel
%A Tanev, Hristo
%Y Afli, Haithem
%Y Bouamor, Houda
%Y Casagran, Cristina Blasi
%Y Ghannay, Sahar
%S Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cartier-tanev-2024-event
%X In this paper we present two approaches for detection of socio political events: the first is based on manually crafted keyword combinations and the second one is based on a BERT classifier. We compare the performance of the two systems on a dataset of socio-political events. Interestingly, the systems demonstrate complementary performance: both showing their best accuracy on non overlapping sets of event types. In the evaluation section we provide insights on the effect of taxonomy mapping on the event detection evaluation. We also review in the related work section the most important resources and approaches for event extraction in the recent years.
%U https://aclanthology.org/2024.politicalnlp-1.2
%P 12-21
Markdown (Informal)
[Event Detection in the Socio Political Domain](https://aclanthology.org/2024.politicalnlp-1.2) (Cartier & Tanev, PoliticalNLP-WS 2024)
ACL
- Emmanuel Cartier and Hristo Tanev. 2024. Event Detection in the Socio Political Domain. In Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024, pages 12–21, Torino, Italia. ELRA and ICCL.