Arabic Named Entity Recognition: Using Features Extracted from Noisy Data

Building an accurate Named Entity Recognition (NER) system for languages with complex morphology is a challenging task. In this paper, we present research that explores the feature space using both gold and bootstrapped noisy features to build an improved highly accurate Arabic NER system. We bootstrap noisy features by projection from an Arabic-English parallel corpus that is automatically tagged with a baseline NER system. The feature space covers lexical, morphological, and syntactic features. The proposed approach

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Yassine Benajiba, Imed Zitouni, Mona T. Diab, Paolo Rosso
• Keywords: Accurate Arabic Ner, ACL 2010, Computational Linguistics, Feature Space, Noisy Features