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In this article, we introduce novel features for Arabic Named Entity Recognition (NER) based on Latent Dirichlet Allocation (LDA), a widely used topic modeling technique. We investigate and analyze three different approaches for utilizing LDA, including two newly proposed ones, namely Topical Prototypes approach and Topical Word Embeddings approach. Our Experiments show that each of the presented approaches improves the baseline features, among which the Word-Class LDA approach performs the best. Moreover, the combination of these topic modeling approaches provides additive improvements, outperforming traditional word representations as Skip-gram word embeddings and Brown Clustering. The proposed LDA-based features, learned in an unsupervised way, are fully language-independent and have proven to be very effective to enrich and boost NER models for Arabic, a morphologically rich language.

Arabic Named Entity Recognition Using Topic Modeling