[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/dan-bareket/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Dan Bareket # Dan Bareket 5 paper cards in Mova's library · 2021–2023 Dan Bareket wrote 5 of the papers Mova reads, in 2021–2023, most of them on [Named entity recognition](https://md.mova.elamur.ai/free/en/docs/papers/topics/named-entity-recognition/); often with [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/), [Amit Seker](https://md.mova.elamur.ai/free/en/docs/papers/authors/amit-seker/), [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/). [OpenAlex](https://openalex.org/A5005137956) · [ORCID](https://orcid.org/0009-0008-2364-5676) · [**h-index 5**](https://openalex.org/authors/A5005137956) · [120 citations](https://openalex.org/authors/A5005137956) · [15 works](https://openalex.org/works?filter=author.id:A5005137956) — 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=Dan+Bareket) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Dan+Bareket) · [dblp](https://dblp.org/search?q=Dan+Bareket) · [ACL Anthology](https://aclanthology.org/search/?q=Dan+Bareket) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Dan+Bareket) Topics: [Named entity recognition](https://md.mova.elamur.ai/free/en/docs/papers/topics/named-entity-recognition/) 2 ## Ideas Mova took (1) From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave. - **kept** — Turn the morpheme head into a deterministic check: a segmenter's output for a token must concatenate back to the surface string. The paper's character-with-boundary view makes this a free invariant to test on every token. *([AlephBERT: Language Model Pre-training and Evaluation from…](https://md.mova.elamur.ai/free/en/docs/papers/acl-2022-seker-alephbert-language-model-pre-training/) · consonant check on every candidate (Model::spells): 0 failures on dev, test and the two-fold cv of train; kept as a gate over cut(); commit 5c24bc409)* ## Papers 1. **[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. 2. **[AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level](https://md.mova.elamur.ai/free/en/docs/papers/acl-2022-seker-alephbert-language-model-pre-training/)** *(2022)* `1 idea kept` Seker et al. pre-train AlephBERT, a BERT-style model for Modern Hebrew, on about 1.9 billion words: the deduplicated Hebrew part of OSCAR, all of Hebrew Wikipedia, and Hebrew tweets, with a 52K wordpiece vocabulary. The base model trained for 10 epochs and took 8 days on eight V100 GPUs. The paper's main point is… 3. **[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. 4. **[AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2021-seker-alephbert-hebrew-large-pre-trained/)** *(2021)* The authors build AlephBERT, a BERT-base style language model for Modern Hebrew, pre-trained on about 1.9 billion words: deduplicated OSCAR, a large set of Hebrew tweets (72% of sentences), and Hebrew Wikipedia. 5. **[Neural Modeling for Named Entities and Morphology (NEMO2)](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2021-bareket-neural-modeling-named-entities-morphology/)** *(2021)* In Hebrew, named-entity boundaries often fall inside a space-delimited token, because a preposition or the definite article is attached to the name. ## Co-authors (12) - [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) ×5 - [Amit Seker](https://md.mova.elamur.ai/free/en/docs/papers/authors/amit-seker/) ×3 - [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/) ×2 - [Cheyn Shmuel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/cheyn-shmuel-shmidman/) ×2 - [Elron Bandel](https://md.mova.elamur.ai/free/en/docs/papers/authors/elron-bandel/) ×2 - [Idan Brusilovsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/idan-brusilovsky/) ×2 - [Moshe Koppel](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-koppel/) ×2 - [Eylon Gueta](https://md.mova.elamur.ai/free/en/docs/papers/authors/eylon-gueta/) - [Joshua Guedalia](https://md.mova.elamur.ai/free/en/docs/papers/authors/joshua-guedalia/) - Refael Greenfeld - [Refael Shaked Greenfeld](https://md.mova.elamur.ai/free/en/docs/papers/authors/refael-shaked-greenfeld/) - [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/) - [← previous Dakuo Wang](https://md.mova.elamur.ai/free/en/docs/papers/authors/dakuo-wang/) - [next → Dan Flickinger](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-flickinger/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)