TAGARAB: a fast, accurate Arabic name recognizer using high-precision morphological analysis
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
The impact of morphological stemming on Arabic mention detection and coreference resolution
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
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
Crime profiling for the Arabic language using computational linguistic techniques
Information Processing and Management: an International Journal
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Named entity recognition (NER) is nowadays an important task, which is responsible for the identification of proper names in text and their classification as different types of named entity such as people, locations, and organizations. In this paper, we present our attempt at the recognition and extraction of the most important proper name entity, that is, the person name, for the Arabic language. We developed the system, Person Name Entity Recognition for Arabic (PERA), using a rule-based approach. The system consists of a lexicon, in the form of gazetteer name lists, and a grammar, in the form of regular expressions, which are responsible for recognizing person name entities. The PERA system is evaluated using a corpus that is tagged in a semi-automated way. The system performance results achieved were satisfactory and confirm to the targets set forth for the precision, recall, and f-measure.