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。

阿拉伯细粒度实体识别

Arabic Fine-Grained Entity Recognition

As more and more Arabic texts emerged on the Internet, extracting important
information from these Arabic texts is especially useful. As a fundamental
technology, Named entity recognition (NER) serves as the core component in
information extraction technology, while also playing a critical role in many
other Natural Language Processing (NLP) systems, such as question answering and
knowledge graph building. In this paper, we provide a comprehensive review of
the development of Arabic NER, especially the recent advances in deep learning
and pre-trained language model. Specifically, we first introduce the background
of Arabic NER, including the characteristics of Arabic and existing resources
for Arabic NER. Then, we systematically review the development of Arabic NER
methods. Traditional Arabic NER systems focus on feature engineering and
designing domain-specific rules. In recent years, deep learning methods achieve
significant progress by representing texts via continuous vector
representations. With the growth of pre-trained language model, Arabic NER
yields better performance. Finally, we conclude the method gap between Arabic
NER and NER methods from other languages, which helps outline future directions
for Arabic NER.

本文全面回顾了阿拉伯语命名实体识别技术的发展，特别是深度学习和预训练语言模型的最新进展，介绍了传统阿拉伯语 NER 系统和最新发展的深度学习方法，并指出了与其他语言 NER 技术的差距，为阿拉伯语 NER 未来的发展方向提供了参考。

阿拉伯命名实体识别综述：历史、最新进展和未来趋势

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

The main objective of this paper is to compare and evaluate the performances
of three open Arabic NER tools: CAMeL, Hatmi, and Stanza. We collected a corpus
consisting of 30 articles written in MSA and manually annotated all the
entities of the person, organization, and location types at the article
(document) level. Our results suggest a similarity between Stanza and Hatmi
with the latter receiving the highest F1 score for the three entity types.
However, CAMeL achieved the highest precision values for names of people and
organizations. Following this, we implemented a "merge" method that combined
the results from the three tools and a "vote" method that tagged named entities
only when two of the three identified them as entities. Our results showed that
merging achieved the highest overall F1 scores. Moreover, merging had the
highest recall values while voting had the highest precision values for the
three entity types. This indicates that merging is more suitable when recall is
desired, while voting is optimal when precision is required. Finally, we
collected a corpus of 21,635 articles related to COVID-19 and applied the merge
and vote methods. Our analysis demonstrates the tradeoff between precision and
recall for the two methods.

该论文比较和评估了三种开放式阿拉伯命名实体识别工具的性能，并集成了这三种工具的结果来识别 COVID-19 相关文章中的命名实体，研究结果表明 CAMeL 在人名和组织名的识别上具有更高的精确度值，而在综合 F1 分数和召回率方面，将三种工具的结果合并可以获得更好的效果。