--- title: "BERT-based Classical Arabic Poetry Authorship Attribution (2025)" description: "Classical Arabic poetry has long had attribution problems. 1 reference, cited by 2 papers here." authors: ["Lama Alqurashi", "Serge Sharoff", "Janet Watson", "Jacob Blakesley"] year: 2025 venue: "Proceedings of the 31st International Conference on Computational Linguistics" source: "https://aclanthology.org/2025.coling-main.409/" 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/alqurashi-2025-bert-based-classical-arabic-poetry/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/alqurashi-2025-bert-based-classical-arabic-poetry/) [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/13/) / BERT-based Classical Arabic Poetry Authorship Attribution # BERT-based Classical Arabic Poetry Authorship Attribution **Authors:** Lama Alqurashi, [Serge Sharoff](https://md.research.elamur.ai/free/en/authors/serge-sharoff/), [Janet Watson](https://md.research.elamur.ai/free/en/authors/janet-watson/) and Jacob Blakesley · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **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.coling-main.409/) · [PDF](https://aclanthology.org/2025.coling-main.409.pdf) **Citations:** [2](https://md.research.elamur.ai/free/en/papers/alqurashi-2025-bert-based-classical-arabic-poetry/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary Classical Arabic poetry has long had attribution problems. Much pre-Islamic verse was transmitted orally and later lost, and some poems composed after the rise of Islam were credited to earlier poets. Earlier computational work on Arabic poetry relied on classic classifiers such as SVM, RF and NB. This paper is among the first to use transformers on this problem, and it works with the whole Classical Arabic Poetry corpus. The authors scraped the corpus from online sources. Because the traditional division into eras is unreliable, they hand-annotated each poet with a Date of Death (DoD). They also used Embedded Topic Modeling (ETM) to label every poem with its topic proportions. An ensemble of CAMeLBERT classifiers, each trained on a different data subset and combined by majority voting, was tested under different settings for the topic, how many poets were included and how many examples were available for training. After tuning, F1 ranged from 0.97 to 1.0, and the ensemble far outperformed a single model. When applied to four disputed pre-Islamic poems, the model's confidence agreed with the doubts raised in historical and literary scholarship. ## Key points - The dataset covers the entire Classical Arabic Poetry corpus, with manually curated DoD metadata replacing traditional era sectioning. - ETM worked best with 5 topics and 50 epochs, which gave the best trade-off between coherence and diversity. Example topics include Praise and Power. - The CAMeLBERT ensemble with majority voting averaged F1 0.98, against 0.75 for a single model. Tuned settings reached F1 0.97–1.0. - Topic-controlled (balanced) training gave slightly higher accuracy and removed low-accuracy outliers. Failure ratios were also computed per topic. - Case studies: - A suspect Imru' al-Qays poem scored 0.71, compared with about 0.99 for his typical poems. - Two doubtful Al-Aʿshā poems scored 0.86 and 0.85. - A debated ʻUbayd Ibn Al-Abraṣ poem scored 0.99, supporting its attribution. ## Interesting ideas - A low confidence score is read as evidence only relative to the class's usual confidence (0.71 against a normal 0.99), not against a global threshold. - Training several copies of one model on different data subsets and taking a majority vote gave much more stable results than a single model. - Balancing topics during training stops the classifier from learning theme instead of style, and per-topic failure ratios reveal where it breaks down. - Precise per-entity metadata (death dates) replaced coarse traditional period labels, which allowed finer modelling of change over time. - The authors admit the model cannot detect multi-author or fabricated texts, and they propose an explicit class for fake items. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): Track each construction's or genre's typical parse confidence in gparse, and flag sentences that fall well below that baseline as candidates for silver-data rejection or repair rules. - **I2** (tree): Train several gparse or perceptron instances on different treebank slices and let them vote as extra witness parsers when building agreed silver trees. - **I3** (learn): Slice tagger and parser errors by domain or topic as a built-in control, so that apparent gains are not just topic memorisation. - **I4** (world): Anchor entities in the knowledge graph to exact Wikidata dates rather than era buckets, so event ordering and timeline questions rest on precise values. - **I5** (answer): Give readers an explicit "not supported / mixed source" outcome, so that when evidence is unclear they report it instead of forcing a best-match answer. *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/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works 🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works ## Cite Cite as: Lama Alqurashi, Serge Sharoff, Janet Watson and Jacob Blakesley. 2025. BERT-based Classical Arabic Poetry Authorship Attribution. Proceedings of the 31st International Conference on Computational Linguistics. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{alqurashi2025bert, title = {{BERT-based Classical Arabic Poetry Authorship Attribution}}, author = {Lama Alqurashi, Serge Sharoff, Janet Watson and Jacob Blakesley}, booktitle = {Proceedings of the 31st International Conference on Computational Linguistics}, year = {2025}, url = {https://aclanthology.org/2025.coling-main.409/} } ``` - [← previous Benchmarking Vision Language Models on German Factual Data](https://md.research.elamur.ai/free/en/papers/peinl-2025-benchmarking-vision-language-models-german/) - [next → Beyond Cairo: Sa'idi Egyptian Arabic Literary Corpus Construction and Analysis](https://md.research.elamur.ai/free/en/papers/eida-2025-beyond-cairo-sa-idi-egyptian/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/13/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work. 1. Abbas, M., Lichouri, M., & Zeggada, A. (2019). Classification of Arabic Poems: From the 5th to the 15th Century. Springer International Publishing Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 11808 LNCS. https://doi.org/10.1007/978-3-030-30754-7_18 [↗](https://doi.org/10.1007/978-3-030-30754-7_18) *Updated 31 October 2026*