  Recent work has shown the effectiveness of the word representations features
in significantly improving supervised NER for the English language. In this
study we investigate whether word representations can also boost supervised NER
in Arabic. We use word representations as additional features in a Conditional
Random Field (CRF) model and we systematically compare three popular neural
word embedding algorithms (SKIP-gram, CBOW and GloVe) and six different
approaches for integrating word representations into NER system. Experimental
results show that Brown Clustering achieves the best performance among the six
approaches. Concerning the word embedding features, the clustering embedding
features outperform other embedding features and the distributional prototypes
produce the second best result. Moreover, the combination of Brown clusters and
word embedding features provides additional improvement of nearly 10% in
F1-score over the baseline.
