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# Uzzi Ornan

3 paper cards in Mova's library · 1995–2014

Uzzi Ornan wrote 3 of the papers Mova reads, in 1995–2014; often with [Alon Itai](https://md.mova.elamur.ai/free/en/docs/papers/authors/alon-itai/), Moshe Levinger.

[OpenAlex](https://openalex.org/A5016702137) · [**h-index 5**](https://openalex.org/authors/A5016702137) · [94 citations](https://openalex.org/authors/A5016702137) · [27 works](https://openalex.org/works?filter=author.id:A5016702137) — 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=Uzzi+Ornan) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Uzzi+Ornan) · [dblp](https://dblp.org/search?q=Uzzi+Ornan) · [ACL Anthology](https://aclanthology.org/search/?q=Uzzi+Ornan) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Uzzi+Ornan)

## Papers

1. **[Innovation of Verbs in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2014-ornan-innovation-verbs-hebrew/)** *(2014)* A short paper on how Hebrew makes new verbs. Hebrew words sit in a root-pattern array: several thousand consonant roots cross 100–120 noun patterns, but only seven verb patterns. When a foreign noun has no verb form, speakers apply "squeezing": the vowels are removed from the noun, the remaining consonants become a…
2. **[A Morphological, Syntactic and Semantic Search Engine for Hebrew Texts](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2002-ornan-morphological-syntactic-semantic-search-engine/)** *(2002)* In unpointed Hebrew a string has on average about three readings, and a plain string search returns mostly wrong hits.
3. **[Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/cl-1995-levinger-learning-morpho-lexical-probabilities-untagged/)** *(1995)* Unpointed Hebrew writing leaves most vowels out, so many word tokens have several morphological analyses; in a sample of press text about 55% of tokens were ambiguous.

## Co-authors (2)

- [Alon Itai](https://md.mova.elamur.ai/free/en/docs/papers/authors/alon-itai/)
- Moshe Levinger
- [← previous University of Haifa](https://md.mova.elamur.ai/free/en/docs/papers/authors/university-of-haifa/)
- [next → Valentina Pyatkin](https://md.mova.elamur.ai/free/en/docs/papers/authors/valentina-pyatkin/)
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