Despite considerable research on the topic of Arabic Named Entity Recognition (NER), almost all efforts focus on a traditional set of semantic classes, features and token representations. In this work, we advance previous research in a systematic manner and devise a novel method to represent these features, relying on a dependency-based structure to capture further evidence within the sentence. Moreover, the work also describes an evaluation of the method involving the capture of global features and employing the clustering of unannotated textual data. To meet this set of goals, we conducted a series of evaluations to evaluate different aspects that demonstrate great improvement when compared with the baseline model.
['Fahd Alotaibi', 'Mark G. Lee']
A Hybrid Approach to Features Representation for Fine-grained Arabic Named Entity Recognition