Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Improvement of a Whole Sentence Maximum Entropy Language Model using grammatical features
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Language model based arabic word segmentation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Markov models for language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Maximum entropy models for named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Maximum entropy models for FrameNet classification
EMNLP '03 Proceedings of the 2003 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
Simplified feature set for Arabic named entity recognition
NEWS '10 Proceedings of the 2010 Named Entities Workshop
RENAR: A Rule-Based Arabic Named Entity Recognition System
ACM Transactions on Asian Language Information Processing (TALIP)
Expert Systems with Applications: An International Journal
Arabic entity graph extraction using morphology, finite state machines, and graph transformations
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Integrating rule-based system with classification for arabic named entity recognition
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Recall-oriented learning of named entities in Arabic Wikipedia
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
A real time Named Entity Recognition system for Arabic text mining
Language Resources and Evaluation
Text mining approach for knowledge extraction in Sahîh Al-Bukhari
Computers in Human Behavior
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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The task of Named Entity Recognition (NER) allows to identify proper names as well as temporal and numeric expressions, in an open-domain text. NER systems proved to be very important for many tasks in Natural Language Processing (NLP) such as Information Retrieval and Question Answering tasks. Unfortunately, the main efforts to build reliable NER systems for the Arabic language have been made in a commercial frame and the approach used as well as the accuracy of the performance are not known. In this paper, we present ANERsys: a NER system built exclusively for Arabic texts based-on n-grams and maximum entropy. Furthermore, we present both the specific Arabic language dependent heuristic and the gazetteers we used to boost our system. We developed our own training and test corpora (ANERcorp) and gazetteers (ANERgazet) to train, evaluate and boost the implemented technique. A major effort was conducted to make sure all the experiments are carried out in the same framework of the CONLL 2002 conference. We carried out several experiments and the preliminary results showed that this approach allows to tackle successfully the problem of NER for the Arabic language.