DOI : https://doi.org/10.18517/ijaseit.7.2.1810

A Comparative Review of Machine Learning for Arabic Named Entity Recognition

Ramzi Esmail Salah (1), Lailatul Qadri binti Zakaria (2)
(1) Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
(2) Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
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How to cite (IJASEIT) :
Salah, Ramzi Esmail, and Lailatul Qadri binti Zakaria. “A Comparative Review of Machine Learning for Arabic Named Entity Recognition”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 2, Apr. 2017, pp. 511-8, doi:10.18517/ijaseit.7.2.1810.
Citation Format :
Arabic Named Entity Recognition (ANER) systems aim to identify and classify Arabic Named entities (NEs) within Arabic text. Other important tasks in Arabic Natural Language Processing (NLP) depends on ANER such as machine translation, question-answering, information extraction, etc. In general, ANER systems can be classified into three main approaches, namely, rule-based, machine-learning or hybrid systems. In this paper, we focus on research progress in machine-learning (ML) ANER and compare between linguistic resource, entity type, domain, method and performance. We also highlight the challenges when processing Arabic NEs through ML systems.

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