Recognition System for Libyan Entity Names

Recognition System for Libyan Entity Names

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Submitted Dec 8, 2020
Published Dec 29, 2020
DOI https://doi.org/10.24018/ejece.2020.4.6.263
Abstract viewed = 176 times
Issue
Vol. 4 No. 6 (2020)
Section
Articles

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  •   Abdelsalam A. Almarimi

    •    Higher Institute of Engineering Technologies Baniwalid, Libya
  •   Ezzedin M. Enbiah

    •    College of Technical Sciences Baniwalid, Libya

Abstract

Named Entity Recognition (NER) is a computational linguistic concept that is used to find and classify appropriate nouns in a text such as person names, geographical locations, and organizations. Such a concept is fundamental in the field of natural language processing. In Libya, many private and public institutions suffer from using the proper translation of entity names from Arabic language into English. Therefore, in this paper, we are concerned with analyzing Arabic articles to extract and recognize entity names. A recognition system is developed for recognizing names of persons, academic institutions, and cities in Libya. At first, a training corpus and dictionaries are built for the intended entity names in this research. Then, the aspects of the entity names are studied, and their patterns and rules are designed. Then, the implementation is performed using Nooj linguistic language. The recognition of person names and Libyan cities and academic institutions was carried out. Statistics showed the frequencies of the appearance rate of person names, academic institutions, and cities in our training corpus. The obtained results are promised and met the research goals for tackling the problem of Arabic named entity recognition.


Keywords: NLP, Arabic Named Entities, Named Entities Recognition, Translations, Transliterations, Nooj Linguistic Platform

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How to Cite
[1]
Almarimi, A.A. and Enbiah, E.M. 2020. Recognition System for Libyan Entity Names. European Journal of Electrical Engineering and Computer Science. 4, 6 (Dec. 2020). DOI:https://doi.org/10.24018/ejece.2020.4.6.263.