Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ismail El Bazi
Sultan Moulay Slimane University
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm Brahim Ait Benali; Soukaina Mihi; Ismail El Bazi; Nabil Laachfoubi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1485-1497

Abstract

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.
Towards an approach based on particle swarm optimization for Arabic named entity recognition on social media Brahim Ait Ben Ali; Soukaina Mihi; Ismail El Bazi; Nabil Laachfoubi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1589-1600

Abstract

Named entity recognition is an essential task for various applications related to natural language processing (NLP). It aims to retrieve a variety of named entities (NEs) from text and categorize them according to predetermined target categories. In many cases, using the entire feature set can be time-consuming and negatively impact the performance. Moreover, it is challenging to find the relevant subsets of features for a particular task due to the high number. The feature selection technique is an unsupervised process for selecting informative features by creating a new subset of informative features. This technique is used to enhance the underlying algorithm's performance. This article implements an effective feature selection algorithm using particle swarm optimization (PSO) to identify and classify the Arabic NEs in the text from social media. PSO is a search algorithm that utilizes a population of particles in a multidimensional space. The proposed method is evaluated using two publicly available Arabic Dialect social media datasets. It is demonstrated through comparisons with both baselines and previous models that the new approach achieves significant accuracy with considerably reduced feature sets in all parameters.
Robust Arabic tweet NER via label-aware data augmentation and AraBERTv2 Brahim Ghazoui; Ismail El Bazi; Ibtissam Essadik; Brahim Ait Benali; Hicham Moussa
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10462

Abstract

Named entity recognition (NER) is vital for turning unstructured social media text into structured information. However, Arabic tweets pose distinct challenges; informality, brevity, dialectal variation, and inconsistent orthography. This study targets those challenges by coupling targeted data augmentation with a transformer model, bert-base-arabertv2. We design a lightweight augmentation pipeline—synonym replacement, name and location replacement, and deletion of third-person Arabic names—to expand linguistic variety and reduce overfitting under limited annotation. The approach is simple, but deliberate: preserve labels when substituting entities with type-consistent alternatives; remove corresponding tags when deleting names; and keep tweet semantics intact where possible. We then fine-tune bert-base-arabertv2 on the combined original and augmented data and evaluate on a held-out set of tweets. The result is a substantial gain in overall performance: F1=0.93 with augmentation versus 0.72 without. These findings indicate that controlled, label-aware augmentation can improve robustness and generalization for Arabic tweet NER, where data scarcity and linguistic variability otherwise degrade accuracy. Beyond empirical gains, our work offers a practical recipe—clear augmentation heuristics and a standard transformer backbone—that can be replicated and adapted to similar low-resource, noisy domains. This contributes to more reliable Arabic social media analysis and downstream information extraction.
Co-Authors Brahim Ait Ben Ali Brahim Ait Benali Brahim Ait Benali Brahim Ghazoui Hicham Moussa Ibtissam Essadik Nabil Laachfoubi Soukaina Mihi