Research to Improve Cross-Language Retrieval - Position Paper for CLEF
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
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COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
RENAR: A Rule-Based Arabic Named Entity Recognition System
ACM Transactions on Asian Language Information Processing (TALIP)
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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Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products. Many researchers have attacked this problem in a variety of languages but only a few limited researches have focused on Named Entity Recognition (NER) for Arabic text due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this paper, we present the results of our attempt at the recognition and extraction of 10 most important named entities in Arabic script; the person name, location, company, date, time, price, measurement, phone number, ISBN and file name. We developed the system, Name Entity Recognition for Arabic (NERA), using a rule-based approach. The system consists of a whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. NERA is evaluated using our own corpora that are tagged in a semi-automated way, and the performance results achieved were satisfactory in terms of precision, recall, and f-measure.