Active Learning for Arabic Text Classification

AL-TAMIMI, Abdel-Karim, BANI-ISAA, Esraa and AL-ALAMI, Ahmed (2021). Active Learning for Arabic Text Classification. In: 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, 123-126.

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Abstract

Active Learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques.

Item Type:	Book Section
Additional Information:	2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 17-18 March 2021, Amity University, Dubai, United Arab Emirates. 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identification Number:	https://doi.org/10.1109/iccike51210.2021.9410758
Page Range:	123-126
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	26 Apr 2023 13:19
Last Modified:	11 Oct 2023 15:45
URI:	https://shura.shu.ac.uk/id/eprint/31044

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