Abstract
We introduce Konooz, a novel multi-dimensional corpus covering 16 Arabic dialects across 10 domains, resulting in 160 distinct corpora. The corpus comprises about 777k tokens, carefully collected and manually annotated with 21 entity types using both nested and flat annotation schemes - using the Wojood guidelines. While Konooz is useful for various NLP tasks like domain adaptation and transfer learning, this paper primarily focuses on benchmarking existing Arabic Named Entity Recognition (NER) models, especially cross-domain and cross-dialect model performance. Our benchmarking of four Arabic NER models using Konooz reveals a significant drop in performance of up to 38% when compared to the in-distribution data. Furthermore, we present an in-depth analysis of domain and dialect divergence and the impact of resource scarcity. We also measured the overlap between domains and dialects using the Maximum Mean Discrepancy (MMD) metric, and illustrated why certain NER models perform better on specific dialects and domains. Konooz is open-source and publicly available at this https URL
Abstract (translated)
我们介绍了Konooz，这是一个新颖的多维度语料库，涵盖了10个领域的16种阿拉伯方言，总计形成了160个独特的语料库。该语料库包含大约777,000个标记，并且这些标记被仔细收集并使用Wojood指南进行了手动标注，包括了21种实体类型和嵌套及扁平化注释方案。虽然Konooz对于各种NLP任务（如领域适应和迁移学习）非常有用，但本文主要关注于利用现有阿拉伯命名实体识别（NER）模型进行基准测试，特别是跨领域的跨方言模型性能。使用Konooz对四种阿拉伯NER模型的基准测试表明，在与分布内数据相比时，这些模型的表现下降了最多达38%。 此外，我们还深入分析了领域和方言差异以及资源稀缺的影响，并利用最大平均不一致度（MMD）指标测量了不同领域的重叠情况。我们展示了为什么某些NER模型在特定的方言和领域中表现更好，并解释了其背后的原因。Konooz是一个开源项目，并且可以在[此处](https://this-URL.com)公开获取。
URL
https://arxiv.org/abs/2506.12615
