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U4RASD claims top spot in Arabic relation extraction challenge

By PulseAugur Editorial · [1 sources] ·

Researchers from U4RASD have detailed their submission to the KnowledgeGraphEval-2026 competition, focusing on Arabic relation extraction. They employed both encoder-based and large language model (LLM) systems to identify one of 40 relations or no relation within Arabic sentences. Their LLM systems utilized fine-tuning techniques like QLoRA on models such as Yehia and Qwen 3.5, incorporating ontology-constrained prediction. An ensemble of their systems achieved a micro-F1 score of 94.06, securing first place in the competition's Subtask 2. AI

IMPACT Demonstrates advancements in Arabic NLP and relation extraction techniques, potentially improving information retrieval and knowledge graph construction.

RANK_REASON The cluster describes a research paper detailing a system's performance in a specific NLP competition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

U4RASD claims top spot in Arabic relation extraction challenge

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The cluster describes a research paper detailing a system's performance in a specific NLP competition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ahmad Abbas, Hussein Mazeh, Chadi Abou Chakra, Aya Jouni, Mohammad Ballout, Fadi A. Zaraket ·

    U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction

    arXiv:2610.12742v1 Announce Type: new Abstract: This paper details our submission to KnowledgeGraphEval 2026 Subtask 2 on directed Arabic relation extraction. We explored encoder-based and large language model approaches for predicting one of 40 relations or no_relation from an A…

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