Event Clustering within News Articles

Faik Kerem Örs, Süveyda Yeniterzi, Reyyan Yeniterzi


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.
Anthology ID:
2020.aespen-1.11
Volume:
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
AESPEN | LREC | WS
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
63–68
Language:
English
URL:
https://www.aclweb.org/anthology/2020.aespen-1.11
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.aespen-1.11.pdf