Abstract
Despite its significance, Arabic, a linguistically rich and morphologically complex language, faces the challenge of being under-resourced. The scarcity of large annotated datasets hampers the development of accurate tools for subjectivity analysis in Arabic. Recent advances in deep learning and Transformers have proven highly effective for text classification in English and French. This paper proposes a new approach for subjectivity assessment in Arabic textual data. To address the dearth of specialized annotated datasets, we developed a comprehensive dataset, AraDhati+, by leveraging existing Arabic datasets and collections (ASTD, LABR, HARD, and SANAD). Subsequently, we fine-tuned state-of-the-art Arabic language models (XLM-RoBERTa, AraBERT, and ArabianGPT) on AraDhati+ for effective subjectivity classification. Furthermore, we experimented with an ensemble decision approach to harness the strengths of individual models. Our approach achieves a remarkable accuracy of 97.79\,\% for Arabic subjectivity classification. Results demonstrate the effectiveness of the proposed approach in addressing the challenges posed by limited resources in Arabic language processing.
Abstract (translated)
尽管阿拉伯语作为一种语言丰富且形态复杂的语言具有重要性，但它面临着资源不足的挑战。大型标注数据集的匮乏阻碍了开发准确进行主观分析工具的发展。最近在深度学习和Transformer方面的进展已经证明对英语和法语文本分类非常有效。本文提出了一种新的方法来评估阿拉伯语文本数据中的主观性。为了应对专门标注数据集缺乏的问题，我们通过利用现有的阿拉伯语数据集和集合（ASTD、LABR、HARD 和 SANAD）开发了一个全面的数据集AraDhati+。随后，我们将最先进的阿拉伯语言模型（XLM-RoBERTa、AraBERT 和 ArabianGPT）在AraDhati+上进行了微调，以实现有效的主观性分类。此外，我们还尝试了集成决策方法来利用各个模型的优势。我们的方法实现了97.79% 的阿拉伯语主观性分类准确率。结果表明，所提出的方法在解决阿拉伯语文本处理中有限资源带来的挑战方面非常有效。
URL
https://arxiv.org/abs/2508.19966
