Alhanoof Althnian, Norah A. Alzahrani, Shaykhah Z. Alsubaie, Eman Albilali, Ahmed Abdelali, Nouf M. Alotaibi, M Saiful Bari, Yazeed Alnumay, Abdulhamed Alothaimen, Maryam Saif, Shahad D. Alzaidi, Faisal Abdulrahman Mirza, Yousef Almushayqih, Mohammed Al Saleem, Ghadah Alabduljabbar, Abdulmohsen Al-Thubaity, Areeb Alowisheq, Nora Al-Twairesh. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Machine reading comprehension aims at understanding a passage and answer a given question by selecting a span from the passage. Recently, pre-trained language models achieved state-of-the-art results on Arabic machine reading comprehension, yet a broad body of works suggests that BERT-based variant models fail to encode and associate common sense facts and world knowledge. To alleviate this weakness, we propose an Arabic knowledge enriched reader model, which fuses external knowledge into contextual representation using an attention and gating mechanism. We learn and generate Arabic knowledge graph embeddings that represent information from Arabic Wikidata and utilize this representation when fusing knowledge. We adopted a knowledge graph embedding scoring function to select the most relevant concepts to the context from the knowledge graph. We evaluated our approach on multiple Arabic machine reading comprehension datasets. Despite leveraging a comparatively smaller pre-trained language model, our approach significantly outperforms large language models in Arabic machine reading comprehension across multiple benchmark datasets, achieving substantial gains in both EM and F1 scores.

Norah Alzahrani, Hisham Alyahya, Yazeed Alnumay, Sultan AlRashed, Shaykhah Alsubaie, Yousef Almushayqih, Faisal Mirza, Nouf Alotaibi, Nora Al-Twairesh, Areeb Alowisheq, M Saiful Bari, Haidar Khan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Sentiment Analysis (SA) is an active research area nowadays due to the tremendous interest in aggregating and evaluating opinions being disseminated by users on the Web.SA of English has been thoroughly researched; however research on SA of Arabic has just flourished.Twitter is considered a powerful tool for disseminating information and a rich resource for opinionated text containing views on many different topics.In this paper we attempt to bridge a gap in Arabic SA of Twitter which is the lack of sentiment lexicons that are tailored for the informal language of Twitter.We generate two lexicons extracted from a large dataset of tweets using two approaches and evaluate their use in a simple lexicon based method.The evaluation is performed on internal and external datasets.The performance of these automatically generated lexicons was very promising, albeit the simple method used for classification.The best F-score obtained was 89.58% on the internal dataset and 63.1-64.7% on the external datasets.

Recently, subjectivity and sentiment analysis of Arabic has received much attention from the research community. In the past two years, an enormous number of references in the field have emerged compared to what has been published in previous years. In this paper, we present an updated survey of the emerging research on subjectivity and sentiment analysis of Arabic. We also highlight the challenges and future research directions in this field.