Extracting numerical data from unstructured Arabic texts (ENAT) - Abeer K. AL-Mashhadany; Dalal N. Hamood; Ahmed T. Sadiq Al-Obaidi; Waleed K. Al-Mashhsdany (Institute of Advanced Engineering and Science, 2021)
Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The inference engine is based on the Arabic linguistic and morphological rules. The applied method encompasses rules of numerical nouns with Arabic morphological rules, in order to achieve high accurate extraction method. Arithmetic operations are applied to convert the numerical phrase into integer value. The proper operation is determined depending on linguistic and morphological rules. It will be shown that applying Arabic linguistic rules together with arithmetic operations succeeded in extracting numerical data from Arabic unstructured text with high accuracy reaches to 100%.