This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location Mention Disambiguation (LMD). The task aimed to evaluate the ability of automated systems to resolve word ambiguity and identify locations mentioned in Arabic text. We provided participants with novel datasets, including a sense-annotated corpus for WSD, called SALMA with approximately 34k annotated tokens, and the IDRISI-DA dataset with 3,893 annotations and 763 unique location mentions. These are challenging tasks. Out of the 38 registered teams, only three teams participated in the final evaluation phase, with the highest accuracy being 77.8% for WSD and the highest MRR@1 being 95.0% for LMD. The shared task not only facilitated the evaluation and comparison of different techniques, but also provided valuable insights and resources for the continued advancement of Arabic NLU technologies.

翻译：本文概述了阿拉伯自然语言理解（ArabicNLU 2024）共享任务，聚焦于词义消歧和地点提及消歧两个子任务。该任务旨在评估自动化系统在阿拉伯语文本中解决词汇歧义和识别地点提及的能力。我们为参与者提供了新颖的数据集，包括用于词义消歧的语义标注语料库SALMA（约含3.4万个标注词元），以及包含3,893条标注和763个独立地点提及的IDRISI-DA数据集。这些任务具有较高挑战性。在38支注册队伍中，仅有三支队伍参与了最终评估阶段，其中词义消歧任务最高准确率为77.8%，地点提及消歧任务最高MRR@1指标达95.0%。本次共享任务不仅促进了不同技术的评估与比较，更为阿拉伯语自然语言理解技术的持续发展提供了宝贵的见解和资源。