C4.5: programs for machine learning
C4.5: programs for machine learning
Using machine learning to maintain rule-based named-entity recognition and classification systems
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition using hundreds of thousands of features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Arabic Named Entity Recognition from Diverse Text Types
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Arabic Natural Language Processing
Arabic Natural Language Processing
ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
NERA: Named Entity Recognition for Arabic
Journal of the American Society for Information Science and Technology
Arabic named entity recognition using optimized feature sets
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
TAGARAB: a fast, accurate Arabic name recognizer using high-precision morphological analysis
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
Voted NER system using appropriate unlabeled data
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Simplified feature set for Arabic named entity recognition
NEWS '10 Proceedings of the 2010 Named Entities Workshop
A hybrid approach to Arabic named entity recognition
Journal of Information Science
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Named Entity Recognition (NER) is a subtask of information extraction that seeks to recognize and classify named entities in unstructured text into predefined categories such as the names of persons, organizations, locations, etc. The majority of researchers used machine learning, while few researchers used handcrafted rules to solve the NER problem. We focus here on NER for the Arabic language (NERA), an important language with its own distinct challenges. This paper proposes a simple method for integrating machine learning with rule-based systems and implement this proposal using the state-of-the-art rule-based system for NERA. Experimental evaluation shows that our integrated approach increases the F-measure by 8 to 14% when compared to the original (pure) rule based system and the (pure) machine learning approach, and the improvement is statistically significant for different datasets. More importantly, our system outperforms the state-of-the-art machine-learning system in NERA over a benchmark dataset.