Name: Arabic Named Entities
Description: we have extracted
  approximately 45,000 Arabic NE
Provider: Multiple institutions
Paper Title: An automatically built Named Entity lexicon for Arabic
  
Tasks: named entity recognition
Venue Name: International Conference on Language Resources and Evaluation
Authors: Mohammed Attia,Antonio Toral,L. Tounsi,M. Monachini,Josef van Genabith
Abstract: We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN’s instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from 
   95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold.