A comparative review of machine learning for Arabic named entity recognition

Ramzi Esmail Salah, Lailatul Qadri Zakaria

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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, questionanswering, 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.

Original languageEnglish
Pages (from-to)511-518
Number of pages8
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume7
Issue number2
DOIs
Publication statusPublished - 2017

Fingerprint

artificial intelligence
Learning systems
Natural Language Processing
Information Storage and Retrieval
Linguistics
translation (genetics)
Processing
Hybrid systems
Research
Machine Learning
methodology

Keywords

  • Arabic named entity recognition
  • Classical Arabic
  • Machine-learning systems
  • Modern standard Arabic

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Computer Science(all)
  • Engineering(all)

Cite this

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