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

Comparing Open Arabic Named Entity Recognition Tools