Effects of Multiple Annotation Schemes on Arabic Named Entity Recognition

Named Entity Recognition (NER) is considered an important subtask in information extraction that aims to identify Named Entities (NM) within a given text and classify them into predefined categories (e.g., person, location, organization, and miscellaneous). The use of an appropriate annotation scheme is crucial to label multi-word NEs and enhance recognition performance. This study investigates the effects of using different annotation schemes on NER systems for the Arabic language. The impact of seven annotation schemes, namely IO, IOB, IOE, IOBE, IOBS, IOES, and IOBES, on Arabic NER is examined by applying conditional random fields, multinomial Naive Bayes, and support vector machine classifiers. The experimental results reveal the importance of selecting an optimal annotation scheme and show that annotating NEs based on the simple IO scheme yields a higher performance in terms of precision, recall, and F-measure compared to the other schemes.