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# Amit Seker

8 paper cards in Mova's library · 2018–2022

Amit Seker wrote 8 of the papers Mova reads, in 2018–2022, most of them on [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/), [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/), [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/), [Dan Bareket](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-bareket/), [Amir More](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-more/).

[OpenAlex](https://openalex.org/A5049561362) · [**h-index 7**](https://openalex.org/authors/A5049561362) · [185 citations](https://openalex.org/authors/A5049561362) · [13 works](https://openalex.org/works?filter=author.id:A5049561362) — 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=Amit+Seker) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Amit+Seker) · [dblp](https://dblp.org/search?q=Amit+Seker) · [ACL Anthology](https://aclanthology.org/search/?q=Amit+Seker) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Amit+Seker)

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 2 · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 2 · [Named entity recognition](https://md.mova.elamur.ai/free/en/docs/papers/topics/named-entity-recognition/) 1

## Ideas Mova took (6)

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)*
- **kept** — Split every wrong prediction into two kinds: "gold analysis missing from the candidate list" and "gold analysis present but not chosen". *([A Pointer Network Architecture for Joint Morphological…](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-seker-pointer-network-architecture-joint-morphological/) · hbo seg eval splits errors into gold missing from the candidates vs present but not chosen, plus oracle@1/2/3/5: unpointed dev 36 missing / 79 not chosen…)*
- **kept** — A small choice model over the segmenter's candidates: the tools/hbo tables already give the candidate splits. *([A Pointer Network Architecture for Joint Morphological…](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-seker-pointer-network-architecture-joint-morphological/) · choice over the lexicon's candidates by the neighbouring words (ctx:0.5, the lexicon share smoothed toward the counts with the word before, then the word…)*
- **kept** — Store the candidates per token as an ordered list, so that a choice is a single index. Log the index with the features that decided it. This matches Mova's "say why" rule and fits the world store. *([A Pointer Network Architecture for Joint Morphological…](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-seker-pointer-network-architecture-joint-morphological/) · Model::candidates gives the ordered candidate list; the choice is logged in MISC (Seg= with the neighbour that decided, Lattice=<label>:<gain>); commits…)*
- **measured, no gain** — MDFirst as a re-ranker: keep the top-B segmentations from the Hebrew segmenter and let the parser rescore each one with its tree score, then take the best joint result. The first target is the segmenter in tools/hbo. *([Joint Transition-Based Models for Morpho-Syntactic Parsing…](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2019-more-joint-transition-based-models-morpho/) · top-k segmentations rescored by gparse's tree score (hbo lattice/choose): no gain beyond noise on Chronicles (unpointed 79.73 -> 79.58..79.99, pointed 85.24…)*
- **measured, no gain** — Keep the lattice: let tools/hbo output every segmentation candidate of a token, with its tag and lemma per arc, and pass the whole lattice to gparse, so that the parser can choose the segmentation that fits the tree. *([What's Wrong with Hebrew NLP? And How to Make it Right](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2019-tsarfaty-what-s-wrong-hebrew-nlp/) · lattice in tools/hbo (hbo lattice / gparse GP_SCORE / hbo choose): the tree score does not pick the gold split (gold candidate +0.13 mean tree gain vs -0.41…)*

## Papers

1. **[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…
2. **[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.
3. **[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.
4. **[A Pointer Network Architecture for Joint Morphological Segmentation and Tagging](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-seker-pointer-network-architecture-joint-morphological/)** *(2020)* `3 ideas kept` Morphological disambiguation (MD) picks one decomposition of each token into morphemes. The authors feed a symbolic lattice of all analyses per token (HebLex for Hebrew, TRMorph2 for Turkish) into a neural pointer network. The lattice is flattened into a sequence of analyses, each analysis is embedded by averaging…
5. **[Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2019-more-joint-transition-based-models-morpho/)** *(2019)* In Hebrew, morphological analysis and disambiguation (MA&D) normally comes before parsing, yet the syntax would help choose the morphology, and the reverse.
6. **[What's Wrong with Hebrew NLP? And How to Make it Right](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2019-tsarfaty-what-s-wrong-hebrew-nlp/)** *(2019)* The paper presents ONLP (YAP), an open system for Modern Hebrew that does segmentation, POS tagging, lemmas, features and labelled dependencies in one package.
7. **[The Hebrew Universal Dependency Treebank: Past, Present and Future](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-sade-hebrew-universal-dependency-treebank-past/)** *(2018)* The Hebrew Treebank (HTB) grew from 500 newspaper sentences (2001) to 6221 sentences, through a chain of automatic and semi-automatic conversions: phrase structure, then Goldberg's functional-head dependencies, then Unified-Stanford, then UD v1 and UD v2.
8. **[Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-seker-universal-morpho-syntactic-parsing-contribution/)** *(2018)* The ONLP Lab submission used UDPipe for segmentation and morphological disambiguation, then yap's arc-eager dependency parser (a Zhang–Nivre variant with morphological-signature features).

## Co-authors (17)

- [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) ×8
- [Dan Bareket](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-bareket/) ×3
- [Amir More](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-more/) ×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
- [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/)
- [Cheyn Shmuel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/cheyn-shmuel-shmidman/)
- [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/)
- [Moshe Koppel](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-koppel/)
- 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/)
- [Shoval Sadde](https://md.mova.elamur.ai/free/en/docs/papers/authors/shoval-sadde/)
- Shoval Sade
- [Stav Klein](https://md.mova.elamur.ai/free/en/docs/papers/authors/stav-klein/)
- Victoria Basmova
- [← previous Amit Roth](https://md.mova.elamur.ai/free/en/docs/papers/authors/amit-roth/)
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