@inproceedings{ors-etal-2020-event,
title = "Event Clustering within News Articles",
author = {{\"O}rs, Faik Kerem and
Yeniterzi, S{\"u}veyda and
Yeniterzi, Reyyan},
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Y{\"o}r{\"u}k, Erdem and
Zavarella, Vanni and
Tanev, Hristo},
booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.aespen-1.11",
pages = "63--68",
abstract = "This paper summarizes our group{'}s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.",
language = "English",
ISBN = "979-10-95546-50-4",
}
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<abstract>This paper summarizes our group’s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Event Clustering within News Articles
%A Örs, Faik Kerem
%A Yeniterzi, Süveyda
%A Yeniterzi, Reyyan
%Y Hürriyetoğlu, Ali
%Y Yörük, Erdem
%Y Zavarella, Vanni
%Y Tanev, Hristo
%S Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-50-4
%G English
%F ors-etal-2020-event
%X This paper summarizes our group’s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.
%U https://aclanthology.org/2020.aespen-1.11
%P 63-68
Markdown (Informal)
[Event Clustering within News Articles](https://aclanthology.org/2020.aespen-1.11) (Örs et al., AESPEN 2020)
ACL
- Faik Kerem Örs, Süveyda Yeniterzi, and Reyyan Yeniterzi. 2020. Event Clustering within News Articles. In Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020, pages 63–68, Marseille, France. European Language Resources Association (ELRA).