EXTRACTING PERSON NAMES USING NAME CANDIDATE INJECTION IN A CONDITIONAL RANDOM FIELD MODEL FOR ARABIC LANGUAGE

Named Entity Recognition and Extraction are very important tasks for discovering proper names including persons, locations, date, and time, inside electronic textual resources. Accurate named entity recognition system is an essential utility to resolve fundamental problems in question answering systems, summary extraction, information retrieval and extraction, machine translation, video interpretation and semantic query expansion. Furthermore, named entity recognition can help us in some state-of-art problems such as removing ambiguity between two common names in different fields, finding out citations in scientific articles, recognizing the associations among persons and improving the results of a search engine to search queries containing named entities.Recently, many researches have been done on named entity recognition for English and other European languages which have led to efficient results; whereas the results are not convincing in Arabic, Persian and many of South Asian languages. One of the most necessary and problematic sub-tasks of named entity recognition is the person named extraction. In this article we have introduced a system for person named extraction in Arabic religious texts using "Proper Name candidate injection" by means of Conditional Random Field (CRF) method. Additionally, we have constructed a new corpus from traditional Arabic religious texts. Applying this method, our experiments have significantly achieved more efficient results.