--- title: "Exploring Author Style in Nakba Short Stories: A Comparative" description: "The authors ask how well transformer encoders can capture shared meaning and individual authorial style in Arabic literary prose. 4 references." authors: ["Osama Hamed", "Nadeem Zaidkilani"] year: 2025 venue: "Proceedings of the first International Workshop on Nakba Narratives as Language Resources" source: "https://aclanthology.org/2025.nakbanlp-1.7/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/hamed-2025-exploring-author-style-nakba-short/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hamed-2025-exploring-author-style-nakba-short/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2025](https://md.research.elamur.ai/free/en/papers/2025/page/4/) / Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models # Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models **Authors:** [Osama Hamed](https://md.research.elamur.ai/free/en/authors/osama-hamed/) and Nadeem Zaidkilani · **Year:** 2025 · **Venue:** Proceedings of the first International Workshop on Nakba Narratives as Language Resources · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2025.nakbanlp-1.7/) · [PDF](https://aclanthology.org/2025.nakbanlp-1.7.pdf) ## Summary The authors ask how well transformer encoders can capture shared meaning and individual authorial style in Arabic literary prose. They work with a small corpus of eight Nakba short stories, each by a different author, taken from the Nakba Archive website. They turned the PDFs into a sentence-per-row CSV by stripping diacritics, Latin characters, URLs and other noise, normalizing whitespace and Tatweel with PyArabic, and splitting sentences with NLTK. Each story becomes a set of contextual embeddings from AraBERT, BERT or RoBERTa, and story pairs are scored with cosine similarity, with TF-IDF as the classical baseline. To limit bias, only stories of comparable length are paired. All three transformers give very high similarity scores (roughly 94–99.8%), while TF-IDF stays near 42–58%, and RoBERTa scores highest on every pair. The authors read this as evidence that transformers capture cultural themes and stylistic nuance in Arabic narratives. They also concede that such near-ceiling scores may hide fine stylistic differences. The cleaned dataset is released publicly as a base for later literary NLP work. ## Key points - Corpus: 8 Nakba short stories by 8 different authors, 473 sentences, about 12,288 tokens, sourced from nakba-archive.org and published at github.com/PsArNLP/Nakba. - Preprocessing tools: PyArabic (diacritic and Tatweel removal, affix stripping), regular expressions for noise, NLTK for sentence splitting and Arabic stopwords, and WordCloud for visualization. - Only three length-matched pairs are compared: stories 2&6, 7&8 and 3&4. - Cosine similarity per pair: - TF-IDF: 54.40 / 42.16 / 57.97 - AraBERT: 96.18 / 93.87 / 95.23 - BERT: 98.23 / 97.89 / 97.77 - RoBERTa: 99.56 / 99.77 / 99.61 - After stemming and stopword removal, the word cloud surfaces the broken plural TwAbyr ("queues", singular TAbur), which the authors tie to daily hardship in refugee camps. ## Interesting ideas - Pairing texts only with others of similar length keeps document size from distorting a similarity score, which is a cheap control for any pairwise comparison. - A plain TF-IDF baseline sits next to the neural models, so the gain from contextual representations is visible as a gap rather than an isolated number. - The authors admit that scores clustered near 99% may fail to separate texts, so a metric that rates everything as similar is a warning sign, not a success. - Stripping affixes and stopwords before counting frequencies exposed a culturally central broken-plural noun that raw counts would have scattered across surface forms. - Converting PDFs into a sentence-per-row table with spare columns for future labels (NER, sentiment) makes later annotation layers easy to attach. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): When comparing outputs from different witness parsers or generated paraphrases, bucket items by length first so agreement scores are not inflated or depressed by size alone. - **I2** (learn): Every evaluation of a frame or world-building step could report a lexical-overlap baseline alongside it, showing how much the structured pipeline adds beyond bag-of-words matching. - **I3** (learn): Add negative controls, such as unrelated or shuffled text pairs, to similarity-based checks, and reject a metric that cannot push those controls clearly below genuine matches. - **I4** (tokens): Aggregating frequencies over lemmas instead of surface forms (irregular plurals, inflections) gives a cleaner signal for spotting a text's key domain words for later stages. - **I5** (world): Store per-sentence records with open metadata slots so entity, state and event annotations from a text's world can be added incrementally and traced back to their source sentence. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works 🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 1,484 works ## Cite Cite as: Osama Hamed and Nadeem Zaidkilani. 2025. Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models. Proceedings of the first International Workshop on Nakba Narratives as Language Resources. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{hamed2025exploring, title = {{Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models}}, author = {Osama Hamed and Nadeem Zaidkilani}, booktitle = {Proceedings of the first International Workshop on Nakba Narratives as Language Resources}, year = {2025}, url = {https://aclanthology.org/2025.nakbanlp-1.7/} } ``` - [← previous Expanding the Universal Dependencies Ancient Hebrew Treebank with Constituency Data](https://md.research.elamur.ai/free/en/papers/swanson-2025-expanding-universal-dependencies-ancient-hebrew/) - [next → Exploring Coreference Resolution in Glosses of German Sign Language](https://md.research.elamur.ai/free/en/papers/bao-2025-exploring-coreference-resolution-glosses-german/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/4/) ## References 4 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Wissam Antoun, Fady Baly, and Hazem Hajj. 2020. [Arabert: Transformer-based model for arabic language understanding](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/). In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, pages 9–15. [↗](https://doi.org/10.48550/arxiv.2003.00104) 2. Prafulla Bafna, Dhanya Pramod, and Anagha Vaidya. 2016. Document clustering: Tf-idf approach. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pages 61–66. IEEE. [find](https://scholar.google.com/scholar?q=Prafulla+Bafna%2C+Dhanya+Pramod%2C+and+Anagha+Vaidya%2E+2016%2E+Document+clustering%3A+Tf%2Didf+approach%2E+In+2016+International+Conference+on+Electrical%2C+Electronics%2C+and+Optimization+Techniques+%28ICEEOT%29%2C+pages+6) 3. Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python. O’Reilly Media Inc. [find](https://scholar.google.com/scholar?q=Steven+Bird%2C+Ewan+Klein%2C+and+Edward+Loper%2E+2009%2E+Natural+Language+Processing+with+Python%2E+O%E2%80%99Reilly+Media+Inc%2E) 4. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. 2018. Universal sentence encoder. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 169–174. [find](https://scholar.google.com/scholar?q=John%2C+Noah+Constant%2C+Mario+Guajardo%2DCespedes%2C+Steve+Yuan%2C+Chris+Tar%2C+et+al%2E+2018%2E+Universal+sentence+encoder%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+Language+Processing%3A) *Updated 24 October 2026*