--- title: "D-Nikud: Enhancing Hebrew Diacritization — Rosenthal 2024" description: "Another Hebrew vocaliser: frozen TavBERT character embeddings, two Bi-LSTM layers and three classifier heads per letter (vowel, dagesh, shin/sin dot).…" authors: ["Adi Rosenthal", "Nadav Shaked"] year: 2024 venue: "arXiv:2402.00075" source: "https://arxiv.org/abs/2402.00075" arxiv: "2402.00075" licence: "CC BY 4.0 (arXiv)" licence_url: "http://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/hbo-2024-rosenthal-d-nikud-enhancing-hebrew-diacritization/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hbo-2024-rosenthal-d-nikud-enhancing-hebrew-diacritization/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/) / D-Nikud: Enhancing Hebrew Diacritization with LSTM and Pretrained Models # D-Nikud: Enhancing Hebrew Diacritization with LSTM and Pretrained Models **Authors:** Adi Rosenthal, Nadav Shaked · **Year:** 2024 · **Venue:** arXiv 2402.00075 (student project, Reichman University) **Link:** [arxiv.org/abs/2402.00075](https://arxiv.org/abs/2402.00075) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) [**0** citations](https://openalex.org/works?filter=cites:W4391505274) OpenAlex, 2026-10-14 · **1** citations in our library of the 140884 papers whose bibliographies we read · [arXiv:2402.00075](https://openalex.org/S4306400194) · [licence](http://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://arxiv.org/abs/2402.00075) - [PDF](https://arxiv.org/pdf/2402.00075) - [arXiv 2402.00075](https://arxiv.org/abs/2402.00075) - [OpenAlex](https://openalex.org/W4391505274) **Authors (2)** 1. [Adi Rosenthal](https://openalex.org/A5036223027) [Semantic Scholar](https://www.semanticscholar.org/author/2137312054) · h 2 · 287 citations · 10 works 2. [Nadav Shaked](https://openalex.org/A5108144964) [ORCID](https://orcid.org/0009-0002-5615-9478) · [Semantic Scholar](https://www.semanticscholar.org/author/2282136256) · h 0 · 0 citations · 3 works Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart. ## What it is about Another Hebrew vocaliser: frozen TavBERT character embeddings, two Bi-LSTM layers and three classifier heads per letter (vowel, dagesh, shin/sin dot). Training data are Nakdimon's corpora (about 1.8 million pre-modern tokens: Ben-Yehuda, Mishneh Torah from Mamre, short stories; and 1.5 million modern tokens auto-pointed by Dicta and corrected) plus Dicta's material, all in full spelling (ktiv male). On Nakdimon's test set it leads on decision and character accuracy (about 98.3 / 97) but trails Dicta on whole-word accuracy; it is best on texts addressed to women, where the gender of second-person forms hides in the vowels. By genre, law and modern prose score highest (CHA ≈ 97–99), while biblical text is worst by far (CHA 90.7, word accuracy 75.7). ## Ideas - I1. The biblical score (three words in four right) shows that modern vocalisers do not transfer to Biblical Hebrew; quote it in the comparison chapter as one more measured reason why Modern Hebrew tools misread the Bible. - I2. Mova's fully pointed Torah is itself a gold test for any vocaliser: strip the points (keep consonants and the matres as written), run the tool, score by the paper's DEC/CHA/WOR/VOC. A cheap measure for a neural teacher, never a part of Mova's answers. - I3. Second-person gender lives in the vowels (ךָ vs ךְ, אַתָּה/אַתְּ, qatal תָּ/תְּ). Check that every second-person form in the trees carries `Gender` and that it agrees with the addressee in `prag`; a disagreement is a converter or data defect. ## For Mova I1 is a sentence in [languages/hbo/12-biblical-mishnaic-modern.md](https://mova.elamur.ai/free/en/docs/languages/hbo/12-biblical-mishnaic-modern/). I2 is a measure described in `13-mova-tools.md`, run only with the operator's word if a model must be installed. I3 is a FEATS check in `tools/macula` and an agreement test between the trees and `tools/macula/src/prag.rs` (addressee), with the rule in `05-pronouns-particles.md`. Topics: [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2402.00075](https://arxiv.org/abs/2402.00075) ```bibtex @misc{rosenthal2024d, title = {{D-Nikud: Enhancing Hebrew Diacritization with LSTM and Pretrained Models}}, author = {Rosenthal, Adi and Shaked, Nadav}, year = {2024}, eprint = {2402.00075}, archivePrefix = {arXiv}, url = {https://arxiv.org/abs/2402.00075} } ``` - [← previous Creating Arabic LLM Prompts at Scale](https://md.research.elamur.ai/free/en/papers/el-2024-sheikh-creating-arabic-llm-prompts-scale/) - [next → Development of Functional Arabic Grammar (Nahwu) Textbooks Based on Local Wisdom for Arabic Language Students](https://md.research.elamur.ai/free/en/papers/azizah-2024-development-functional-arabic-grammar-nahwu/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/) ## References 3 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Elazar Gershuni and Yuval Pinter, Restoring Hebrew Diacritics Without a Dictionary, 2022, March, https://arxiv.org/pdf/2105.05209.pdf. [5] Sepp Hochreiter and Jurgen Schmidhuber, LONG SHORT-TERM MEMORY, 1997, April, https://www.bioinf.jku.at/publications/older/2604.pdf, MIT Press. [↗](https://arxiv.org/abs/2105.05209) [our card](https://md.research.elamur.ai/free/en/papers/findings-2022-gershuni-restoring-hebrew-diacritics-without-dictionary/) 2. Avi Shmidman and Shaltiel Shmidman and Moshe Koppel and Yoav Goldberg, Nakdan: Professional Hebrew Diacritizer, 2020, March, [https://arxiv.org/pdf/2005.03312.pdf](https://arxiv.org/pdf/2005.03312.pdf). [6] Stav Klein and Reut Tsarfaty, Getting the life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?, 2020, July, https://aclanthology.org/2020. sigmorphon-1.24. [↗](https://arxiv.org/pdf/2005.03312.pdf) [our card](https://md.research.elamur.ai/free/en/papers/aclbulk-2020-shmidman-nakdan-professional-hebrew-diacritizer/) 3. Omri Keren and Tal Avinari and Reut Tsarfaty and Omer Levy, Breaking Character: Are Subwords Good Enough for MRLs After All?, 2022, April, https://arxiv.org/pdf/2204.04748.pdf. [↗](https://arxiv.org/abs/2204.04748)