Traditional NER systems are typically trained to recognize coarse-grained
entities, and less attention is given to classifying entities into a hierarchy
of fine-grained lower-level subtypes. This article aims to advance Arabic NER
with fine-grained entities. We chose to extend Wojood (an open-source Nested
Arabic Named Entity Corpus) with subtypes. In particular, four main entity
types in Wojood, geopolitical entity (GPE), location (LOC), organization (ORG),
and facility (FAC), are extended with 31 subtypes. To do this, we first revised
Wojood's annotations of GPE, LOC, ORG, and FAC to be compatible with the LDC's
ACE guidelines, which yielded 5, 614 changes. Second, all mentions of GPE, LOC,
ORG, and FAC (~44K) in Wojood are manually annotated with the LDC's ACE
sub-types. We refer to this extended version of Wojood as WojoodF ine. To
evaluate our annotations, we measured the inter-annotator agreement (IAA) using
both Cohen's Kappa and F1 score, resulting in 0.9861 and 0.9889, respectively.
To compute the baselines of WojoodF ine, we fine-tune three pre-trained Arabic
BERT encoders in three settings: flat NER, nested NER and nested NER with
subtypes and achieved F1 score of 0.920, 0.866, and 0.885, respectively. Our
corpus and models are open-source and available at
this https URL

该研究论文旨在通过扩展 Wojood（一种开源的阿拉伯语嵌套命名实体语料库）的细分类型，提升阿拉伯语 NER 的准确性和细粒度，通过改变 Wojood 的注释，手动标注扩展 Wojood，并在三种设置下使用预训练的阿拉伯 BERT 编码器计算 WojoodFine 的基准，其 F1 分数分别为 0.920, 0.866, 和 0.885。