Event-Arguments Extraction Corpus and Modeling using BERT for Arabic
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Abstract (arXiv)
Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the hadath 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 testNLI 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 small urlhttps://sina.birzeit.edu/wojood
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 30 Jul 2024.
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16 of 36 references.
References · 16
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- 2022Wojood: Nested Arabic Named Entity Corpus and Recognition using BERTcited by 33
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- 2022Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?cited by 13
- 2024Are Large Language Models the New Interface for Data Pipelines?cited by 13
- 2023SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarkscited by 10
4 more
- 2023Arabic Fine-Grained Entity Recognitioncited by 7
- 2023WojoodNER 2023: The First Arabic Named Entity Recognition Shared Taskcited by 6
- 2024WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Taskcited by 6
- 2024Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and Propagandacited by 4
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.