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        <title>Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models</title>
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            <title>Proceedings of the first International Workshop on Nakba Narratives as Language Resources</title>
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            <namePart type="given">Mustafa</namePart>
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    <abstract>Measuring semantic similarity and analyzing authorial style are fundamental tasks in Natural Language Processing (NLP), with applications in text classification, cultural analysis, and literary studies. This paper investigates the semantic similarity and stylistic features of Nakba short stories, a key component of Palestinian literature, using transformer-based models, AraBERT, BERT, and RoBERTa. The models effectively capture nuanced linguistic structures, cultural contexts, and stylistic variations in Arabic narratives, outperforming the traditional TF-IDF baseline. By comparing stories of similar length, we minimize biases and ensure a fair evaluation of both semantic and stylistic relationships. Experimental results indicate that RoBERTa achieves slightly higher performance, highlighting its ability to distinguish subtle stylistic patterns. This study demonstrates the potential of AI-driven tools to provide more in-depth insights into Arabic literature, and contributes to the systematic analysis of both semantic and stylistic elements in Nakba narratives.</abstract>
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%0 Conference Proceedings
%T Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models
%A Hamed, Osama
%A Zaidkilani, Nadeem
%Y Jarrar, Mustafa
%Y Habash, Habash
%Y El-Haj, Mo
%S Proceedings of the first International Workshop on Nakba Narratives as Language Resources
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F hamed-zaidkilani-2025-exploring
%X Measuring semantic similarity and analyzing authorial style are fundamental tasks in Natural Language Processing (NLP), with applications in text classification, cultural analysis, and literary studies. This paper investigates the semantic similarity and stylistic features of Nakba short stories, a key component of Palestinian literature, using transformer-based models, AraBERT, BERT, and RoBERTa. The models effectively capture nuanced linguistic structures, cultural contexts, and stylistic variations in Arabic narratives, outperforming the traditional TF-IDF baseline. By comparing stories of similar length, we minimize biases and ensure a fair evaluation of both semantic and stylistic relationships. Experimental results indicate that RoBERTa achieves slightly higher performance, highlighting its ability to distinguish subtle stylistic patterns. This study demonstrates the potential of AI-driven tools to provide more in-depth insights into Arabic literature, and contributes to the systematic analysis of both semantic and stylistic elements in Nakba narratives.
%U https://aclanthology.org/2025.nakbanlp-1.7/
%P 56-62

Markdown (Informal)

[Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models](https://aclanthology.org/2025.nakbanlp-1.7/) (Hamed & Zaidkilani, NakbaNLP 2025)

• Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models (Hamed & Zaidkilani, NakbaNLP 2025)

ACL

• Osama Hamed and Nadeem Zaidkilani. 2025. Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models. In Proceedings of the first International Workshop on Nakba Narratives as Language Resources, pages 56–62, Abu Dhabi. Association for Computational Linguistics.