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# Moshe Koppel

14 paper cards in Mova's library · 2011–2026

Moshe Koppel wrote 14 of the papers Mova reads, in 2011–2026, most of them on [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/), [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/); often with [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/), [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/), [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/).

[OpenAlex](https://openalex.org/A5019908505) · [**h-index 37**](https://openalex.org/authors/A5019908505) · [7124 citations](https://openalex.org/authors/A5019908505) · [150 works](https://openalex.org/works?filter=author.id:A5019908505) — 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=Moshe+Koppel) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Moshe+Koppel) · [dblp](https://dblp.org/search?q=Moshe+Koppel) · [ACL Anthology](https://aclanthology.org/search/?q=Moshe+Koppel) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Moshe+Koppel)

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 2 · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1

## Ideas Mova took (1)

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…)*

## 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. **[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.
3. **[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.
4. **[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…
5. **[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.
6. **[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.
7. **[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.
8. **[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.
9. **[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).
10. **[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.
11. **[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.
12. **[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…
13. **[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.
14. **[Unsupervised Decomposition of a Document into Authorial Components](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2011-koppel-unsupervised-decomposition-document-into-authorial/)** *(2011)* The question is whether the authorial layers of a composite text can be separated without any labelled example.

## Co-authors (18)

- [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/) ×13
- [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/) ×10
- [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) ×4
- [Cheyn Shmuel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/cheyn-shmuel-shmidman/) ×3
- [Joshua Guedalia](https://md.mova.elamur.ai/free/en/docs/papers/authors/joshua-guedalia/) ×3
- [Amir DN Cohen](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-dn-cohen/) ×2
- [Dan Bareket](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-bareket/) ×2
- Amir David Nissan Cohen
- [Amit Seker](https://md.mova.elamur.ai/free/en/docs/papers/authors/amit-seker/)
- Eli Handel
- Ely Porat
- [Eylon Gueta](https://md.mova.elamur.ai/free/en/docs/papers/authors/eylon-gueta/)
- [Hillel Gershuni](https://md.mova.elamur.ai/free/en/docs/papers/authors/hillel-gershuni/)
- Idan Dershowitz
- [Nachum Dershowitz](https://md.mova.elamur.ai/free/en/docs/papers/authors/nachum-dershowitz/)
- Navot Akiva
- Ometz Shmidman
- [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/)
- [← previous Morris Alper](https://md.mova.elamur.ai/free/en/docs/papers/authors/morris-alper/)
- [next → Moshe Lavee](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-lavee/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)