NAMED ENTITY RECOGNITION FOR QURANIC TEXT USING RULE BASED APPROACHES

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially Named Entity Recognition where different contain several types of entities. current NER model is deemed not fit to accurately extract entities from Quranic text due its unique content. This paper describes building a rule-based method that exist English translation meaning performance evaluation. entity tagging, common task in-text annotation, which (nouns) unstructured are identified assigned class. A few rules built such as name prophets people, creation, location, time, various names God. mainly using regular expressions gazetteers. have been result high precision recall well satisfactory F-score over 90%. results this experiment can be used annotation machine learning same type domain specifically on or generally Islamic text.