A Novel Hybrid Approach to Arabic Named Entity Recognition

Abstract

Named Entity Recognition (NER) task is an essential preprocessing task for many Natural Language Processing (NLP) applications such as text summarization, document categorization, Information Retrieval, among others. NER systems follow either rule-based approach or machine learning approach. In this paper, we introduce a novel NER system for Arabic using a hybrid approach, which combines a rule-based approach and a machine learning approach in order to improve the performance of Arabic NER. The system is able to recognize three types of named entities, including Person, Location and Organization. Experimental results on ANERcorp dataset showed that our hybrid approach has achieved better performance than using the rule-based approach and the machine learning approach when they are processed separately. It also outperforms the state-of-the-art hybrid Arabic NER systems.