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# Avi Shmidman

22 paper cards in Mova's library · 2016–2026

Avi Shmidman wrote 22 of the papers Mova reads, in 2016–2026, most of them on [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/), [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/); often with [Moshe Koppel](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-koppel/), [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/), [Cheyn Shmuel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/cheyn-shmuel-shmidman/).

[OpenAlex](https://openalex.org/A5013212409) · [**h-index 5**](https://openalex.org/authors/A5013212409) · [122 citations](https://openalex.org/authors/A5013212409) · [53 works](https://openalex.org/works?filter=author.id:A5013212409) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Avi+Shmidman) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Avi+Shmidman) · [dblp](https://dblp.org/search?q=Avi+Shmidman) · [ACL Anthology](https://aclanthology.org/search/?q=Avi+Shmidman) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Avi+Shmidman)

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 2 · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 2

## Ideas Mova took (2)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

- **measured, no gain** — Constrain prefix segmentation by a table of legal proclitic sets keyed on the word's initial letters. *([DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/arxivrecent-2023-shmidman-dictabert-state-art-bert-suite/) · legal prefix-chain table as a hard filter (chain:N, learned chains): split right dev 97.38 / 94.21 unchanged, cv +-0.01 (pointed, unpointed); the learned…)*
- **kept** — Constrain by a wordlist: in unpointed mode, restrict each token's candidates to forms in Mova's lexicon, then score them. *([NLP in the DH pipeline: Transfer-learning to a Chronolect](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2021-rubinstein-nlp-dh-pipeline-transfer-learning/) · host-lexicon check on unseen words (hostlex:b): hard filter hurts (names cut into known hosts: pointed dev 97.38 -> 95.77); soft b=0.3 kept for pointed (with…)*

## Papers

1. **[Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2026-shmidman-dicta-lm-3-0-advancing/)** *(2026)* Dicta-LM 3.0 is an open-weight family of Hebrew-and-English LLMs at 24B, 12B and 1.7B parameters. Each was made by continued pre-training of an existing base model (Mistral-Small-3.1, Nemotron Nano V2, Qwen3-1.7B) on about 100B Hebrew tokens mixed with 30B English tokens, then post-trained with SFT, DPO and GRPO for…
2. **[Human-AI Annotation Error Auditing for Hebrew Diacritization with Frontier LLMs](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-gershuni-human-ai-annotation-error-auditing/)** *(2026)* The authors audit the labels of the EACL 2023 Hebrew Homograph Challenge Set (about 150,000 sentences, niqqud on homographs in Modern Hebrew) for annotation errors.
3. **[Modeling the Dalet Clitic in Historical Hebrew Texts: A New Prefix-Segmented BERT Model and Stylistic Analysis](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-tal-modeling-dalet-clitic-historical-hebrew/)** *(2026)* The Aramaic proclitic dalet in historical Hebrew can mark a subordinate clause or a possessive preposition, and the two uses look the same on the page; no annotated data existed.
4. **[A New Hebrew Universal Dependency Treebank: The First Treebank of Post-Rabbinic Historical Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/ud-2025-tal-new-hebrew-universal-dependency-treebank/)** *(2025)* The authors release the first UD treebank of post-Rabbinic historical Hebrew: about 11,800 words from six works (Igeret orhot olam, Sefer ha-hinukh, Rashi's Pentateuch commentary, Shulhan arukh, Sefer Maharil and a miscellany), written between the tenth and nineteenth centuries.
5. **[Automatic Text Segmentation of Ancient and Historic Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2025-rosensweig-automatic-text-segmentation-ancient-historic/)** *(2025)* 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.
6. **[Irony Detection in Hebrew Documents: A Novel Dataset and an Evaluation of Neural Classification Methods](https://md.mova.elamur.ai/free/en/docs/papers/prag-2025-shmidman-irony-detection-hebrew-documents-novel/)** *(2025)* Hebrew has no capital letters, so a single word in quotation marks can mean three different things: a name (naming, e.g.
7. **[NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2025-shmidman-neodictabert-pushing-frontier-bert-models/)** *(2025)* The authors move the NeoBERT encoder design (pre-norm RMSNorm, RoPE, SwiGLU, 28 layers at width 768, 4,096-token context) to Hebrew.
8. **[Restoring Missing Spaces in Scraped Hebrew Social Media](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2025-shmidman-restoring-missing-spaces-scraped-hebrew/)** *(2025)* Scraped web text often runs two words together, and in unvocalised Hebrew one conflated string can usually be split in several valid ways, so a dictionary lookup does not settle it.
9. **[MRL Parsing Without Tears: The Case of Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/findings-2024-shmidman-mrl-parsing-without-tears-case/)** *(2024)* Parsers for morphologically rich languages either run a pipeline (segment, tag, parse), which passes errors forward, or a joint lattice over all segmentations, which is slow and needs a lexicon.
10. **[MsBERT: A New Model for the Reconstruction of Lacunae in Hebrew Manuscripts](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-msbert-new-model-reconstruction-lacunae/)** *(2024)* The authors pretrain a BERT-base model, MsBERT, on Hebrew manuscript transcriptions. The paper argues that existing Hebrew BERTs, including BEREL trained on printed rabbinic texts, break on manuscript orthography: extra matres lectionis, acronyms written with apostrophes, truncated words, and the proclitic לש. The…
11. **[Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities](https://md.mova.elamur.ai/free/en/docs/papers/arxivrecent-2024-shmidman-adapting-llms-hebrew-unveiling-dictalm/)** *(2024)* The paper adapts Mistral-7B to Hebrew by continued pre-training on about 200B tokens of half Hebrew and half English.
12. **[Computational Methods for the Analysis of Complementizer Variability in Language and Literature: The Case of Hebrew "she-" and "ki"](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-computational-methods-analysis-complementizer-variability/)** *(2024)* Modern Hebrew has two complementizers, še- and ki, that are usually described as interchangeable with ki as the more formal register.
13. **[OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-otobert-identifying-suffixed-verbal-forms/)** *(2024)* Modern Hebrew literature sometimes fuses a verb with its direct-object pronoun into one word. The fused form is often identical to a common verb with another meaning, so readers are misled until the sentence ends. Annotators found 222 suffixed verbs in 135K words of literature (17.76 per 10K) against 7 in 185K words…
14. **[Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?](https://md.mova.elamur.ai/free/en/docs/papers/eacl-2023-shmidman-do-pretrained-contextual-language-models/)** *(2023)* Unpointed Hebrew is full of homographs whose analyses differ in segmentation (the prefix or suffix is or is not a word), in morphosyntax, or only in sense.
15. **[DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/arxivrecent-2023-shmidman-dictabert-state-art-bert-suite/)** *(2023)* The paper presents DictaBERT, a BERT-architecture model for Modern Hebrew, pre-trained with masked language modelling only (next-sentence prediction removed) on about 3.8B tokens: 70% cleaned HeDC4 web text and 30% news, blogs, subtitles and novels.
16. **[Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-shmidman-introducing-dictalm-large-generative-language/)** *(2023)* The authors release DictaLM, a 7B-parameter generative model for Modern Hebrew, trained on about 7.5 billion tokens.
17. **[Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2022-shmidman-introducing-berel-bert-embeddings-rabbinic/)** *(2022)* A short paper releasing a BERT-base model trained on about 220 million words of Rabbinic Hebrew (the Sefaria and Dicta libraries).
18. **[Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2022-gueta-large-pre-trained-models-extra/)** *(2022)* The authors train AlephBERTGimmel, a Modern Hebrew BERT with a 128K word-piece vocabulary (earlier models had 2.4K–52K), on the same ~2 billion words as AlephBERT.
19. **[NLP in the DH pipeline: Transfer-learning to a Chronolect](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2021-rubinstein-nlp-dh-pipeline-transfer-learning/)** *(2021)* `1 idea kept` The authors build a diacritizer (vowel marks) for Emergent Modern Hebrew, the Hebrew of the early twentieth century, using a hand-vocalised goldset of 144,446 words from the Jerusalem Corpus of Emergent Modern Hebrew (129,453 words of literature, 14,993 of ephemera).
20. **[Nakdan: Professional Hebrew Diacritizer](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2020-shmidman-nakdan-professional-hebrew-diacritizer/)** *(2020)* Nakdan adds vowel points to undiacritized Hebrew, for modern, Rabbinic and poetic texts, through a web interface that lets an editor choose among the alternatives.
21. **[A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration](https://md.mova.elamur.ai/free/en/docs/papers/findings-2020-shmidman-novel-challenge-set-hebrew-morphological/)** *(2020)* Hebrew homographs are often unbalanced: one analysis is 30 to 129 times more frequent than the other. A tagger can then score well on the majority reading and fail on the minority one, which is the reading that matters in rare cases. The authors build a challenge set of 21 homographs (12 unbalanced, 9 balanced) with…
22. **[Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2016-shmidman-identification-parallel-passages-across-large/)** *(2016)* A fast method for finding all approximate parallel passages (20+ words) in a large Hebrew/Aramaic corpus, where spelling (plene vs defective, final letters, א/י), particles and synonyms vary.

## Co-authors (27)

- [Moshe Koppel](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-koppel/) ×13
- [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/) ×12
- [Cheyn Shmuel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/cheyn-shmuel-shmidman/) ×4
- [Joshua Guedalia](https://md.mova.elamur.ai/free/en/docs/papers/authors/joshua-guedalia/) ×4
- [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) ×4
- [Hillel Gershuni](https://md.mova.elamur.ai/free/en/docs/papers/authors/hillel-gershuni/) ×3
- [Amir DN Cohen](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-dn-cohen/) ×2
- [Aynat Rubinstein](https://md.mova.elamur.ai/free/en/docs/papers/authors/aynat-rubinstein/) ×2
- [Dan Bareket](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-bareket/) ×2
- [Rachel Tal](https://md.mova.elamur.ai/free/en/docs/papers/authors/rachel-tal/) ×2
- Amir David Nissan Cohen
- [Amit Seker](https://md.mova.elamur.ai/free/en/docs/papers/authors/amit-seker/)
- Avishay Gerczuk
- Benjamin Resnick
- Elda Weizman
- Eli Handel
- Elisha Rosensweig
- Elisheva Brauner
- Ely Porat
- Ephraim Meiri
- [Eylon Gueta](https://md.mova.elamur.ai/free/en/docs/papers/authors/eylon-gueta/)
- [Nachum Dershowitz](https://md.mova.elamur.ai/free/en/docs/papers/authors/nachum-dershowitz/)
- Ometz Shmidman
- Orly Albeck
- Shlomit Fuchs
- Yitzchak Lindenbaum
- [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/)
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