Arabic rule-based named entity recognition systems: Progress and challenges

Abstract

Rule-based approaches are using human-made rules to extract Named Entities (NEs), it is one of the most famous ways to extract NE as well as Machine Learning. The term Named Entity Recognition (NER) is defined as a task determined to indicate personal names, locations, organizations and many other entities. In Arabic language, Big Data challenges make Arabic NER develops rapidly and extract useful information from texts. The current paper sheds some light on research progress in rule-based via a diagnostic comparison among linguistic resource, entity type, domain, and performance. We also highlight the challenges of the processing Arabic NEs through rule-based systems. It is expected that good performance of NER will be effective to other modern fields like semantic web searching, question answering, machine translation, information retrieval, and abstracting systems.