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
Voyellation automatique de l'arabe
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
Arabic named entity recognition: using features extracted from noisy data
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
A real time Named Entity Recognition system for Arabic text mining
Language Resources and Evaluation
Hi-index | 0.00 |
Research on automatic recognition of named entities from Arabic text uses techniques that work well for the Latin based languages such as local grammars, statistical learning models, pattern matching, and rule-based techniques. These techniques boost their results by using application specific corpora, parallel language corpora, and morphological stemming analysis. We propose a method for extracting entities, events, and relations amongst them from Arabic text using a hierarchy of finite state machines driven by morphological features such as part of speech and gloss tags, and graph transformation algorithms. We evaluated our method on two natural language processing applications. We automated the extraction of narrators and narrator relations from several corpora of Islamic narration books. We automated the extraction of genealogical family trees from Biblical texts. In all applications, our method reports high precision and recall and learns lemmas about phrases that improve results.