Arabic Named Entity Recognition from Diverse Text Types

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.