[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/michael-elhadad/)

[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/) / Michael Elhadad

# Michael Elhadad

15 paper cards in Mova's library · 1996–2020

Michael Elhadad wrote 15 of the papers Mova reads, in 1996–2020, most of them on [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/), [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/), [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/); often with [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/), [Meni Adler](https://md.mova.elamur.ai/free/en/docs/papers/authors/meni-adler/), [Yael Dahan Netzer](https://md.mova.elamur.ai/free/en/docs/papers/authors/yael-dahan-netzer/).

[OpenAlex](https://openalex.org/A5013658527) · [ORCID](https://orcid.org/0000-0002-5629-2351) · [**h-index 25**](https://openalex.org/authors/A5013658527) · [3695 citations](https://openalex.org/authors/A5013658527) · [137 works](https://openalex.org/works?filter=author.id:A5013658527) — 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=Michael+Elhadad) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Michael+Elhadad) · [dblp](https://dblp.org/search?q=Michael+Elhadad) · [ACL Anthology](https://aclanthology.org/search/?q=Michael+Elhadad) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Michael+Elhadad)

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 2 · [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) 2 · [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 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 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** — Joint segmentation and parsing as a Mova measure: run tools/hbo in a mode where all segmentations from its tables stay as arcs, and compare its one-best pipeline with the lattice reading on a dev split of treebanks/hbo/ptnk. *([Joint Hebrew Segmentation and Parsing using a PCFGLA…](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2011-goldberg-joint-hebrew-segmentation-parsing-pcfgla/) · one-best vs lattice reading measured on dev and Chronicles (hbo lattice/choose): no gain; unpointed dev 77.09 best 77.39 (tuned) but Chronicles -0.10 at that…)*

## Papers

1. **[Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2020-eyal-building-hebrew-semantic-role-labeling/)** *(2020)* The authors build the first Hebrew semantic role resource by projecting English SRL, produced by SEMAFOR, onto aligned Hebrew sentences from OpenSubtitles 2016.
2. **[The Hebrew FrameNet Project](https://md.mova.elamur.ai/free/en/docs/papers/frame-2016-hayoun-hebrew-framenet-project/)** *(2016)* The authors describe a Hebrew FrameNet built as a semi-automatic translation of English FrameNet 1.5.1. They chose the 200 frames that cover the most frequent English words, and found that English lexical units cover about 66% of their Hebrew corpus of 1.75M sentences. Dictionary translations (about 49K pairs) speed…
3. **[Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System](https://md.mova.elamur.ai/free/en/docs/papers/cl-2013-goldberg-word-segmentation-unknown-word-resolution/)** *(2013)* The paper builds a constituency parser for Modern Hebrew on the BerkeleyParser (PCFG with latent annotations) and extends it for a small treebank of 6,220 sentences.
4. **[Multi-document multilingual summarization corpus preparation, Part 2: Czech, Hebrew and Spanish](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2013-elhadad-multi-document-multilingual-summarization-corpus/)** *(2013)* This is the second part of the report on the MultiLing 2013 corpus, which covers ten languages; this part covers Czech, Hebrew and Spanish.
5. **[Effect of Out Of Vocabulary Terms on Inferring Eligibility Criteria for a Retrospective Study in Hebrew EHR](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2013-cohen-effect-out-vocabulary-terms-inferring/)** *(2013)* The authors classify Hebrew clinical notes from a paediatric neurology unit (894 visits by 516 patients in 2009, about 226K tokens) as eligible or not for a retrospective epilepsy study.
6. **[Joint Hebrew Segmentation and Parsing using a PCFGLA Lattice Parser](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2011-goldberg-joint-hebrew-segmentation-parsing-pcfgla/)** *(2011)* Hebrew function words are written attached to the next word, so one space-delimited token can have several segmentations, and a pipeline that segments first cannot use the syntax to choose.
7. **[Easy-First Dependency Parsing of Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2010-goldberg-easy-first-dependency-parsing-modern/)** *(2010)* The authors apply their easy-first, non-directional dependency parser to the Hebrew dependency treebank, a small treebank of a language with rich morphology and free word order.
8. **[Hebrew Dependency Parsing: Initial Results](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2009-goldberg-hebrew-dependency-parsing-initial-results/)** *(2009)* The authors derive a Hebrew dependency treebank (DepTB) from version 2 of the Hebrew constituency treebank.
9. **[Tagging a Hebrew Corpus: the Case of Participles](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2008-adler-tagging-hebrew-corpus-case-participles/)** *(2008)* The authors build a part-of-speech and morphology corpus of Modern Hebrew news (about 40M tokens, with a 200K-token sample tagged by hand).
10. **[SVM Model Tampering and Anchored Learning: A Case Study in Hebrew NP Chunking](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2007-goldberg-svm-model-tampering-anchored-learning/)** *(2007)* The paper studies an SVM chunker for Hebrew Simple NPs (about 4,500 training sentences, 500 test, 27K chunks) and opens the trained model in two ways.
11. **[An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-adler-unsupervised-morpheme-based-hmm-hebrew/)** *(2006)* Adler and Elhadad disambiguate Hebrew morphology with an unsupervised HMM. The only resource is a morphological analyser, with no tagged corpus. Because the morphemes of a word cannot be seen, only the words they form, each morpheme slot in the sentence is a lattice node that records its possible predecessors and…
12. **[Noun Phrase Chunking in Hebrew: Influence of Lexical and Morphological Features](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-goldberg-noun-phrase-chunking-hebrew-influence/)** *(2006)* The traditional base-NP definition (non-recursive NPs, as in English) gives tiny, one-word chunks on the Hebrew Treebank, so the authors define "Simple NPs" instead.
13. **[Generating Determiners and Quantifiers in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-1998-netzer-generating-determiners-quantifiers-hebrew/)** *(1998)* The paper is one part of HUGG, a Hebrew generation grammar built to share an input specification with the English grammar SURGE.
14. **[Generation of Noun Compounds in Hebrew: Can Syntactic Knowledge Be Fully Encapsulated?](https://md.mova.elamur.ai/free/en/docs/papers/syn-1998-netzer-generation-noun-compounds-hebrew-can/)** *(1998)* Hebrew smixut, the marked noun-noun construct (ben ha-melekh, "son of the king", against ben melekh), is very productive and makes up about 40% of noun modifiers in the authors' corpus.
15. **[An Overview of SURGE: a Reusable Comprehensive Syntactic Realization Component](https://md.mova.elamur.ai/free/en/docs/papers/elhadad-1996-overview-surge-reusable-comprehensive-syntactic/)** *(1996)* A short demo paper on SURGE, a syntactic realisation front-end for generators, written in FUF, a functional unification formalism that carries a systemic grammar of English.

## Co-authors (12)

- [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/) ×7
- [Meni Adler](https://md.mova.elamur.ai/free/en/docs/papers/authors/meni-adler/) ×3
- [Yael Dahan Netzer](https://md.mova.elamur.ai/free/en/docs/papers/authors/yael-dahan-netzer/) ×2
- Avi Hayoun
- Ben Eyal
- David Gabay
- George Giannakopoulos
- Jacques Robin
- Josef Steinberger
- Raphael Cohen
- Sabino Miranda-Jiménez
- Yael Netzer
- [← previous Michael Collins](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-collins/)
- [next → Michael Franke](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-franke/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)