@inproceedings{hassaneen-etal-2026-isnad,
title = "{ISNAD} at {K}nowledge{G}raph{E}val 2026: {A}rabic Relation Extraction via Entity-Aware {A}ra{BERT} and Imbalance-Aware Resampling",
author = "Hassaneen, Mena Allah Ahmed and
Atawneh, Bayan and
Salah, Hani",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.152/",
pages = "1212--1218",
ISBN = "979-8-89176-497-2",
abstract = "Relation extraction (RE) is a fundamental component of knowledge graph construction, question answering, and text summarization. Although recent RE models achieve strong overall performance through the fine-tuning of pretrained language models, their aggregate performance can obscure substantial disparities across relation types, particularly under long-tailed distributions. This issue remains largely unexplored for Arabic RE. In this work, we investigate the effectiveness of class-balancing strategies for long-tail relation extraction in Arabic by fine-tuning AraBERT with entity markers on Wojood$^{Relations}$. Our system, submitted to the KnowledgeGraphEval 2026 shared task, achieves a Micro-F1 score of 92.73 on the official test set. Further analysis shows that errors are disproportionately concentrated among low-frequency and semantically overlapping relation types, indicating that imbalance-handling strategies developed primarily for English benchmarks do not fully address the challenges posed by the long-tailed relation distribution in Arabic."
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<abstract>Relation extraction (RE) is a fundamental component of knowledge graph construction, question answering, and text summarization. Although recent RE models achieve strong overall performance through the fine-tuning of pretrained language models, their aggregate performance can obscure substantial disparities across relation types, particularly under long-tailed distributions. This issue remains largely unexplored for Arabic RE. In this work, we investigate the effectiveness of class-balancing strategies for long-tail relation extraction in Arabic by fine-tuning AraBERT with entity markers on Wojood^Relations. Our system, submitted to the KnowledgeGraphEval 2026 shared task, achieves a Micro-F1 score of 92.73 on the official test set. Further analysis shows that errors are disproportionately concentrated among low-frequency and semantically overlapping relation types, indicating that imbalance-handling strategies developed primarily for English benchmarks do not fully address the challenges posed by the long-tailed relation distribution in Arabic.</abstract>
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%0 Conference Proceedings
%T ISNAD at KnowledgeGraphEval 2026: Arabic Relation Extraction via Entity-Aware AraBERT and Imbalance-Aware Resampling
%A Hassaneen, Mena Allah Ahmed
%A Atawneh, Bayan
%A Salah, Hani
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F hassaneen-etal-2026-isnad
%X Relation extraction (RE) is a fundamental component of knowledge graph construction, question answering, and text summarization. Although recent RE models achieve strong overall performance through the fine-tuning of pretrained language models, their aggregate performance can obscure substantial disparities across relation types, particularly under long-tailed distributions. This issue remains largely unexplored for Arabic RE. In this work, we investigate the effectiveness of class-balancing strategies for long-tail relation extraction in Arabic by fine-tuning AraBERT with entity markers on Wojood^Relations. Our system, submitted to the KnowledgeGraphEval 2026 shared task, achieves a Micro-F1 score of 92.73 on the official test set. Further analysis shows that errors are disproportionately concentrated among low-frequency and semantically overlapping relation types, indicating that imbalance-handling strategies developed primarily for English benchmarks do not fully address the challenges posed by the long-tailed relation distribution in Arabic.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.152/
%P 1212-1218
Markdown (Informal)
[ISNAD at KnowledgeGraphEval 2026: Arabic Relation Extraction via Entity-Aware AraBERT and Imbalance-Aware Resampling](https://aclanthology.org/2026.arabicnlp-sharedtasks.152/) (Hassaneen et al., ArabicNLP 2026)
ACL