--- title: "Automatic Text Segmentation of Ancient and Historic Hebrew — Rosensweig 2025" description: "They compare segmenters: few-shot completion with the DictaLM 2.0 base model, masked-language-model prediction of… 12 references, cited by 2 papers here." authors: ["Elisha Rosensweig", "Benjamin Resnick", "Hillel Gershuni", "Joshua Guedalia", "Nachum Dershowitz", "Avi Shmidman"] year: 2025 venue: "Proceedings of the Second Workshop on Ancient Language Processing" source: "https://aclanthology.org/2025.alp-1.1/" doi: "10.18653/v1/2025.alp-1.1" 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/rosensweig-2025-automatic-text-segmentation-ancient-historic/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/rosensweig-2025-automatic-text-segmentation-ancient-historic/) [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/2/) / Automatic Text Segmentation of Ancient and Historic Hebrew # Automatic Text Segmentation of Ancient and Historic Hebrew **Authors:** Elisha Rosensweig; Benjamin Resnick; [Hillel Gershuni](https://md.research.elamur.ai/free/en/authors/hillel-gershuni/); [Joshua Guedalia](https://md.research.elamur.ai/free/en/authors/joshua-guedalia/); [Nachum Dershowitz](https://md.research.elamur.ai/free/en/authors/nachum-dershowitz/); [Avi Shmidman](https://md.research.elamur.ai/free/en/authors/avi-shmidman/) · **Year:** 2025 · **Venue:** Proceedings of the Second Workshop on Ancient Language Processing **Link:** [aclanthology.org/2025.alp-1.1](https://aclanthology.org/2025.alp-1.1/) · library `aclbulk-2025-rosensweig-automatic-text-segmentation-ancient-historic` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology) [**0** citations](https://openalex.org/works?filter=cites:W4411113297) OpenAlex, 2026-10-14 · [**2** citations in our library](https://md.research.elamur.ai/free/en/papers/rosensweig-2025-automatic-text-segmentation-ancient-historic/cited-by/) of the 140,884 papers whose bibliographies we read · [Proceedings of the Second Workshop on Ancient Language Processing](https://md.research.elamur.ai/free/en/venues/alp/) · published by [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · [licence](https://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://aclanthology.org/2025.alp-1.1/) - [PDF](https://aclanthology.org/2025.alp-1.1.pdf) - [DOI 10.18653/v1/2025.alp-1.1](https://doi.org/10.18653/v1/2025.alp-1.1) - [OpenAlex](https://openalex.org/W4411113297) **Authors (6)** 1. [Elisha Rosensweig](https://openalex.org/A5064825146) [Semantic Scholar](https://www.semanticscholar.org/author/2083169737) · h 9 · 624 citations · 16 works 2. Benjamin Resnick [Semantic Scholar](https://www.semanticscholar.org/author/2391518695) 3. [Hillel Gershuni](https://openalex.org/A5107544595) [Semantic Scholar](https://www.semanticscholar.org/author/2139215888) · h 1 · 1 citations · 8 works 4. [Joshua Guedalia](https://openalex.org/A5059119329) [ORCID](https://orcid.org/0000-0002-3458-7896) · [Semantic Scholar](https://www.semanticscholar.org/author/2358660049) · h 14 · 487 citations · 48 works 5. [Nachum Dershowitz](https://openalex.org/A5039993352) [ORCID](https://orcid.org/0000-0003-0363-2735) · [Semantic Scholar](https://www.semanticscholar.org/author/1759551) · h 43 · 8,852 citations · 511 works 6. [Avi Shmidman](https://openalex.org/A5013212409) [Semantic Scholar](https://www.semanticscholar.org/author/3361477) · h 5 · 114 citations · 53 works Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart. ## What it is about Unpunctuated Rabbinic and historic Hebrew and Aramaic texts run to hundreds of words without a full stop, and most neural tools need input cut into chunks. The authors annotate a new ground-truth set of 25 texts (7,265 words, one annotator) with three marker levels: Break (B), Partial (P), and Maybe (M), where M positions are clearly better than random but should not be cut. They compare segmenters: few-shot completion with the DictaLM 2.0 base model, masked-language-model prediction of punctuation with several Hebrew BERT models (HeBERT, AlephBERT, DictaBERT, BEREL), the multilingual Segment-Any-Text model, and instruction-prompted GPT-4o and Claude Sonnet. The aim is chunks of at most about 50 words. BEREL, trained on Rabbinic Hebrew, is the best masked-model option and rivals the LLMs without their instability; Segment-Any-Text does well but trails BEREL. ## Ideas - I2. Score segmenters at three strictness levels (B; B+P; B+P+M), as the paper does, so that a rule is judged both on precision and on how often it breaks a sentence badly. - I3. Use the 25-text set as a held-out test for the chunker, with the 50-word cap as a hard check. Run it once per release, not during tuning. ## For Mova - I1. A rule-based chunker before parsing: cut first at the B positions, then at P positions when a chunk would pass 50 words, and never at M positions unless nothing else is possible. Each cut then has a reason, and the rule can be run on `tools/hbo` input before `gparse`. - I4. Cut positions as clause-boundary evidence: the B and P markers are the same kind of boundary that the parser's clause rules need, so a chunker that misses them is a signal for [languages/hbo/08-syntax.md](https://mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/). I1 to I3 belong in `tools/hbo`, in the segmentation step before `gparse`, with the test set kept held out. I4 is a note for [languages/hbo/08-syntax.md](https://mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/). The paper's best system is a masked model; Mova should take the measurement protocol and the task, and build the rule, not the model. ## Institutions [🎓**Bar-Ilan University** Ramat Gan · Israel 102 works · 17,587 citations](https://md.research.elamur.ai/free/en/universities/bar-ilan-university/) · [🎓**Tel Aviv University** Tel Aviv · Israel 54 works · 7,966 citations](https://md.research.elamur.ai/free/en/universities/tel-aviv-university/) 📚 Shelves 4[Computer Science · 7,204 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 7,042 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 7,381 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 7,325 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 3[Semitic languages · 1,336 works](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Hebrew & Aramaic · 474 works](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) · [Biblical Hebrew · 254 works](https://md.research.elamur.ai/free/en/topics/biblical-hebrew/) ## Cite Cite as Elisha Rosensweig, Benjamin Resnick, Hillel Gershuni, Joshua Guedalia, Nachum Dershowitz and Avi Shmidman. 2025. Automatic Text Segmentation of Ancient and Historic Hebrew. Proceedings of the Second Workshop on Ancient Language Processing. The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.alp-1.1](https://doi.org/10.18653/v1/2025.alp-1.1) ```bibtex @inproceedings{rosensweig2025automatic, title = {{Automatic Text Segmentation of Ancient and Historic Hebrew}}, author = {Elisha Rosensweig, Benjamin Resnick, Hillel Gershuni, Joshua Guedalia, Nachum Dershowitz and Avi Shmidman}, booktitle = {Proceedings of the Second Workshop on Ancient Language Processing}, year = {2025}, doi = {10.18653/v1/2025.alp-1.1}, url = {https://doi.org/10.18653/v1/2025.alp-1.1} } ``` - [← previous AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data](https://md.research.elamur.ai/free/en/papers/alshaikh-2025-aratable-benchmarking-llms-reasoning-understanding/) - [next → Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases](https://md.research.elamur.ai/free/en/papers/aldahoul-2025-benchmarking-legal-reasoning-llms-arabic/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/2/) ## References 12 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Avihay Chriqui and Inbal Yahav. 2021. HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. Preprint, arXiv:2102.01909. [↗](https://arxiv.org/abs/2102.01909) [our card](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/) 2. Aitor Alvarez, Carlos-D Martínez-Hinarejos, Haritz Arzelus, Marina Balenciaga, and Arantza del Pozo. 2017. Improving the automatic segmentation of subtitles through conditional random field. Speech Communication, 88:83–95. [find](https://scholar.google.com/scholar?q=Aitor+Alvarez%2C+Carlos%2DD+Mart%C3%ADnez%2DHinarejos%2C+Haritz+Arzelus%2C+Marina+Balenciaga%2C+and+Arantza+del+Pozo%2E+2017%2E+Improving+the+automatic+segmentation+of+subtitles+through+conditional+random+field%2E+Speech+Co) 3. Eep Talstra Centre for Bible and Computer. 2023. Bhsa: Biblia hebraica stuttgartensia amstelodamen-sis. [https://github.com/ETCBC/bhsa](https://github.com/ETCBC/bhsa). Accessed: 2025-03-18. [↗](https://github.com/ETCBC/bhsa) 4. Dennis Aumiller, Satya Almasian, Sebastian Lackner, and Michael Gertz. 2021. Structural text segmentation of legal documents. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pages 2–11. [find](https://scholar.google.com/scholar?q=Dennis+Aumiller%2C+Satya+Almasian%2C+Sebastian+Lackner%2C+and+Michael+Gertz%2E+2021%2E+Structural+text+segmentation+of+legal+documents%2E+In+Proceedings+of+the+Eighteenth+International+Conference+on+Artificial+In) 5. Federico Errica, Giuseppe Siracusano, Davide Sanvito, and Roberto Bifulco. 2024. What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering. Preprint, arXiv:2406.12334. [↗](https://arxiv.org/abs/2406.12334) 6. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam Mc-Candlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. Preprint, arXiv:2005.14165. [↗](https://arxiv.org/abs/2005.14165) [our card](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/) 7. Markus Frohmann, Igor Sterner, Ivan Vulić, Benjamin Minixhofer, and Markus Schedl. 2024. "Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation". In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11908– 11941, Miami, Florida, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Markus+Frohmann%2C+Igor+Sterner%2C+Ivan+Vulic%CC%81%2C+Benjamin+Minixhofer%2C+and+Markus+Schedl%2E+2024%2E+%22Segment+Any+Text%3A+A+Universal+Approach+for+Robust%2C+Efficient+and+Adaptable+Sentence+Segmentation%22%2E+In+Proceed) 8. Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, and Dong Yu. 2020. Recurrent chunking mechanisms for long-text machine reading comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6751– 6761, Online. Association for Computational Linguistics. 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[find](https://scholar.google.com/scholar?q=Toward+Unifying+Text+Amir+Hazem%2C+B%C3%A9atrice+Daille%2C+Dominique+Stutzmann%2C+Christopher+Kermorvant%2C+and+Louis+Chevalier%2E+2020%2E+Hierarchical+text+segmentation+for+medieval+manuscripts%2E+In+Proceedings+of+the) 12. A Timo Homburg and Christian Chiarcos. 2016. Akkadian word segmentation. In Proceedings Tenth International Conference on Language Resource Evaluation.(LREC 2016), pages 4067–4074. A.1 [find](https://scholar.google.com/scholar?q=A+Timo+Homburg+and+Christian+Chiarcos%2E+2016%2E+Akkadian+word+segmentation%2E+In+Proceedings+Tenth+International+Conference+on+Language+Resource+Evaluation%2E%28LREC+2016%29%2C+pages+4067%E2%80%934074%2E+A%2E1) Updated 23 October 2026