  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.
