{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:25:05Z","timestamp":1769163905182,"version":"3.49.0"},"reference-count":27,"publisher":"Association for Computing Machinery (ACM)","issue":"7","funder":[{"DOI":"10.13039\/100009392","name":"Prince Sattam bin Abdulaziz University","doi-asserted-by":"crossref","award":["PSAU\/2025\/R\/1446"],"award-info":[{"award-number":["PSAU\/2025\/R\/1446"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Event coreference resolution is a critical task in Natural Language Processing (NLP), enabling applications such as information extraction, text summarization, and question answering. However, resolving event coreference in Arabic presents unique challenges due to the language\u2019s rich morphology, complex syntax, and lack of annotated resources. This article introduces\n            <jats:italic toggle=\"yes\">AraEventCoref<\/jats:italic>\n            , the first publicly available Arabic event coreference dataset, comprising 50 annotated news articles with 1,381 events and 159 coreference chains. The dataset\u2019s annotation agreement achieved a CoNLL score of 75.8%, ensuring high reliability across\n            <jats:italic toggle=\"yes\">B<\/jats:italic>\n            <jats:sup>3<\/jats:sup>\n            , MUC, and\n            <jats:italic toggle=\"yes\">\n              CEAF\n              <jats:sub>e}<\/jats:sub>\n            <\/jats:italic>\n            metrics. Additionally, event triggers were annotated with an inter-annotator agreement of 96% using Cohen\u2019s Kappa, further validating dataset quality. To establish benchmarks, we developed a fine-tuned CamelBERT-msa model as a strong baseline and evaluated state-of-the-art Arabic large language models (LLMs) using both bilingual and Arabic-only prompts. Results demonstrate the effectiveness of fine-tuning for domain-specific adaptation and reveal the impact of bilingual prompting on LLM performance. By providing a high-quality dataset and benchmarking results, this work lays a foundation for advancing Arabic event coreference research and supports future developments in event relation extraction.\n          <\/jats:p>","DOI":"10.1145\/3743047","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T07:11:53Z","timestamp":1749107513000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["AraEventCoref: An Arabic Event Coreference Dataset and LLM Benchmarks"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5145-1907","authenticated-orcid":false,"given":"Mohammed","family":"Aldawsari","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University","place":["Al Kharj, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0305-0092","authenticated-orcid":false,"given":"Omer","family":"Dawood","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University","place":["Al Kharj, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Omar Einea Ashraf Elnagar and Ridhwan Al Debsi. 2019. Sanad: Single-label arabic news articles dataset for automatic text categorization. Data in Brief 25 (2019) 104076.","DOI":"10.1016\/j.dib.2019.104076"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Mohammed Aldawsari Manjur Kolhar and Omer Salih Dawood Omer. 2023. Within-document arabic event coreference: Challenges datasets approaches and future direction. Applied Sciences 13 19 (2023) 11004.","DOI":"10.3390\/app131911004"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.275"},{"key":"e_1_3_2_5_2","first-page":"4545","volume-title":"LREC","author":"Cybulska Agata","year":"2014","unstructured":"Agata Cybulska and Piek Vossen. 2014. Using a sledgehammer to crack a nut? Lexical diversity and event coreference resolution. 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Lisbon, 837\u2013840."},{"key":"e_1_3_2_9_2","first-page":"143","volume-title":"Proceedings of the Seventeenth Conference on Computational Natural Language Learning","author":"Pradhan Sameer","year":"2013","unstructured":"Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Bj\u00f6rkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013. Towards robust linguistic analysis using ontonotes. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning. 143\u2013152."},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Loic De Langhe Orph\u00e9e De Clercq and Veronique Hoste. 2023. Constructing a cross-document event coreference corpus for Dutch. 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