@inproceedings{aljabari-etal-2024-event,
title = "Event-Arguments Extraction Corpus and Modeling using {BERT} for {A}rabic",
author = "Aljabari, Alaa and
Duaibes, Lina and
Jarrar, Mustafa and
Khalilia, Mohammed",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.26",
doi = "10.18653/v1/2024.arabicnlp-1.26",
pages = "309--319",
abstract = "Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the corpus (550k tokens) as an extension of Wojood, enriched with event-argument annotations. We used three types of event arguments: $agent$, $location$, and $date$, which we annotated as relation types. Our inter-annotator agreement evaluation resulted in 82.23{\%} $Kappa$ score and 87.2{\%} $F_1$-score. Additionally, we propose a novel method for event relation extraction using BERT, in which we treat the task as text entailment. This method achieves an $F_1$-score of 94.01{\%}.To further evaluate the generalization of our proposed method, we collected and annotated another out-of-domain corpus (about 80k tokens) called and used it as a second test set, on which our approach achieved promising results (83.59{\%} $F_1$-score). Last but not least, we propose an end-to-end system for event-arguments extraction. This system is implemented as part of SinaTools, and both corpora are publicly available at \url{https://sina.birzeit.edu/wojood}",
}
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<abstract>Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the corpus (550k tokens) as an extension of Wojood, enriched with event-argument annotations. We used three types of event arguments: agent, location, and date, which we annotated as relation types. Our inter-annotator agreement evaluation resulted in 82.23% Kappa score and 87.2% F₁-score. Additionally, we propose a novel method for event relation extraction using BERT, in which we treat the task as text entailment. This method achieves an F₁-score of 94.01%.To further evaluate the generalization of our proposed method, we collected and annotated another out-of-domain corpus (about 80k tokens) called and used it as a second test set, on which our approach achieved promising results (83.59% F₁-score). Last but not least, we propose an end-to-end system for event-arguments extraction. This system is implemented as part of SinaTools, and both corpora are publicly available at https://sina.birzeit.edu/wojood</abstract>
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%0 Conference Proceedings
%T Event-Arguments Extraction Corpus and Modeling using BERT for Arabic
%A Aljabari, Alaa
%A Duaibes, Lina
%A Jarrar, Mustafa
%A Khalilia, Mohammed
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F aljabari-etal-2024-event
%X Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the corpus (550k tokens) as an extension of Wojood, enriched with event-argument annotations. We used three types of event arguments: agent, location, and date, which we annotated as relation types. Our inter-annotator agreement evaluation resulted in 82.23% Kappa score and 87.2% F₁-score. Additionally, we propose a novel method for event relation extraction using BERT, in which we treat the task as text entailment. This method achieves an F₁-score of 94.01%.To further evaluate the generalization of our proposed method, we collected and annotated another out-of-domain corpus (about 80k tokens) called and used it as a second test set, on which our approach achieved promising results (83.59% F₁-score). Last but not least, we propose an end-to-end system for event-arguments extraction. This system is implemented as part of SinaTools, and both corpora are publicly available at https://sina.birzeit.edu/wojood
%R 10.18653/v1/2024.arabicnlp-1.26
%U https://aclanthology.org/2024.arabicnlp-1.26
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.26
%P 309-319
Markdown (Informal)
[Event-Arguments Extraction Corpus and Modeling using BERT for Arabic](https://aclanthology.org/2024.arabicnlp-1.26) (Aljabari et al., ArabicNLP-WS 2024)
ACL