Arabic named entity recognition using conditional random fields
Y. Benajiba, Paolo Rosso · 2008
The Named Entity Recognition (NER) task consists in determining and classifying proper names within an open-domain text. This Natural Language Processing task proved to be harder for languages with a complex morphology such as the Arabic language. NER was also proved to help Natural Language Processing tasks such as Machine Translation, Information Retrieval and Question Answering to obtain a higher performance. In our previous works we have presented the first and the second version of ANERsys: an Arabic Named Entity Recognition system, whose performance we have succeeded to improve by more than 10 points, from the first to the second version, by adopting a different architecture and using additional information such as Part-Of-Speech tags and Base Phrase Chunks. In this paper, we present a further attempt to enhance the accuracy of ANERsys by changing the probabilistic model from Maximum Entropy to Conditional Random Fields which helped to improve the results significantly. 1.