论文标题
比较开放的阿拉伯语命名实体识别工具
Comparing Open Arabic Named Entity Recognition Tools
论文作者
论文摘要
本文的主要目的是比较和评估三种开放式阿拉伯语工具的性能：骆驼，哈特米和斯坦扎。我们收集了一个由MSA编写的30篇文章组成的语料库，并在文章（文档）级别手动注释了人，组织和位置类型的所有实体。我们的结果表明，STANZA和HATMI之间的相似性，后者获得了三种实体类型的最高F1分数。但是，骆驼获得了人和组织名称的最高精度价值。此后，我们实施了一种“合并”方法，该方法将三个工具的结果和一个“投票”方法组合在一起，该方法仅在三个将它们识别为实体时，该方法命名实体。我们的结果表明，合并达到了最高的整体F1分数。此外，合并具有最高的召回值，而投票的三种实体类型的精度值最高。这表明当需要召回时，合并更合适，而当需要精确度时，投票是最佳的。最后，我们收集了与Covid-19有关的21,635篇文章的语料库，并应用了合并和投票方法。我们的分析证明了两种方法的精确度和回忆之间的权衡。
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.
