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; often with Reut Tsarfaty, Amit Seker, Avi Shmidman.

OpenAlex · ORCID · h-index 5 · 120 citations · 15 works — 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 · Semantic Scholar · dblp · ACL Anthology · ORCID

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… · 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

Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses? (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.
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level (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…
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All (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.
AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With (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.
Neural Modeling for Named Entities and Morphology (NEMO2) (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)

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-bareket/