Arabic question answering system: a survey

Alwaneen, Tahani; Azmi, Aqil M.; Aboalsamh, Hatim; Wang, Zhaoxia; Hussain, Amir

doi:10.1007/s10462-021-10031-1

Cited by 35 publications

(20 citation statements)

References 63 publications

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“…Thus, the best existing methods for ABSA are adapted or modified to Arabic and other languages. Arabic is a challenging and lowresource language [18]; thus, few methods have been proposed for this language. Table 1 lists and compares previous studies that have considered the Arabic language.…”

Section: Multi-task Learning For Absamentioning

confidence: 99%

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Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Fadel¹,

Abulnaja²,

Saleh³

2023

Computers, Materials &Amp; Continua

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Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the ATE task is the base task for APC, and its result impacts the accuracy of APC. Thus, in this study, we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a single model. This paper integrates the multi-task model, namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification (LCF-ATEPC) and Arabic Bidirectional Encoder Representation from Transformers (AraBERT) as a shred layer for Arabic contextual text representation. The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism (LCF) to capture the interactive information between an aspect and its context. Moreover, data augmentation techniques are proposed based on state-of-the-art augmentation techniques (word embedding substitution with constraints and contextual embedding (AraBERT)) to increase the diversity of the training dataset. This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA. Extensive experiments were conducted on the original and combined datasets (merging the original and augmented datasets). Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques. Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer (AR-LCF-ATEPC-Fusion) and the proposed data augmentation word embedding-based method (FastText) on the combined dataset.

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Section: Multi-task Learning For Absamentioning

confidence: 99%

“…Arabic has a richer inflectional and derivative morphology. There is a limited number of resources and tools for Arabic, so there is a lack of language resources and tools [18,19]. Thus, only a limited number of studies have attempted to develop Arabic ABSA (AABSA) methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Fadel¹,

Abulnaja²,

Saleh³

2023

Computers, Materials &Amp; Continua

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“…The review written by [ 15 ] discussed Arabic language processing (ALP) and modeling techniques for question answering system. The authors also discussed all the existing methods, datasets and processing techniques for the task, and elaborated on the challenges and limitations.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the small number of studies addressing fake news detection in Arabic, we also revised prior work in other languages such as articles [14], [15], [16] and [17] who discussed English fake news detection using different techniques. In [27], while examining English fake news detection, the authors proposed a two-step approach for detecting fake news in social media.…”

Section: Similarmentioning

confidence: 99%

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Arabic fake news detection based on deep contextualized embedding models

Nassif

Elnagar

Elgendy

et al. 2022

Neural Comput & Applic

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Social media is becoming a source of news for many people due to its ease and freedom of use. As a result, fake news has been spreading quickly and easily regardless of its credibility, especially in the last decade. Fake news publishers take advantage of critical situations such as the Covid-19 pandemic and the American presidential elections to affect societies negatively. Fake news can seriously impact society in many fields including politics, finance, sports, etc. Many studies have been conducted to help detect fake news in English, but research conducted on fake news detection in the Arabic language is scarce. Our contribution is twofold: first, we have constructed a large and diverse Arabic fake news dataset. Second, we have developed and evaluated transformer-based classifiers to identify fake news while utilizing eight state-of-the-art Arabic contextualized embedding models. The majority of these models had not been previously used for Arabic fake news detection. We conduct a thorough analysis of the state-of-the-art Arabic contextualized embedding models as well as comparison with similar fake news detection systems. Experimental results confirm that these state-of-the-art models are robust, with accuracy exceeding 98%.

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