Uzzi Ornan


Innovation of Verbs in Hebrew
Uzzi Ornan
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media


A Morphological, Syntactic and Semantic Search Engine for Hebrew Texts
Uzzi Ornan
Proceedings of the ACL-02 Workshop on Computational Approaches to Semitic Languages


Machine translation by semantic features
Uzzi Ornan | Israel Gutter
Proceedings of the International Conference on Machine Translation and Multilingual Applications in the new Millennium: MT 2000


Monolingual translator workstation
Guy Bashkansky | Uzzi Ornan
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

Although the problem of full machine translation (MT) is unsolved yet, the computer aided translation (CAT) makes progress. In this field we created a work environment for monolingual translator 1. This package of tools generally enables a user who masters a source language to translate texts to a target language which the user does not master. The application is for Hebrew-to-Russian case, emphasizing specific problems of these languages, but it can be adapted for other pairs of languages also. After Source Text Preparation, Morphological Analysis provides all the meanings for every word. The ambiguity problem is very serious in languages with incomplete writing, like Hebrew. But the main problem is the translation itself. Words’ meanings mapping between languages is M:M, i.e., almost every source word has a number of possible translations, and almost every target word can be a translation of several words. Many methods for resolving of these ambiguities propose using large data bases, like dictionaries with semantic fields based on θ-theory. The amount of information needed to deal with general texts is prohibitively large. We propose here to solve ambiguities by a new method: Accumulation with Inversion and then Weighted Selection, plus Learning, using only two regular dictionaries: from source to target and from target to source languages. The method is built from a number of phases: (1) during Accumulation with Inversion, all the possible translations to the target language of every word are brought, and every one of them is translated back to the source language; (2) Selection of suitable suggestions is being made by user in source language, this is the only manual phase; (3) Weighting of the selection’s results is being made by software and determines the most suitable translation to the target language; (4) Learning of word’s context will provide preferable translation in the future. Target Text Generation is based on morphological records in target language, that are produced by the disambiguation phase. To complete the missing features for word’s building, we propose here a method of Features Expansion. This method is based on assumptions about feature flow through the sentence, and on dependence of grammatical phenomena in the two languages. Software of the workstation combines four tools: Source Text Preparation, Morphological Analysis, Disambiguation and Target Text Generation. The application includes an elaborated windows interface, on which the user’s work is based.


Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew
Moshe Levinger | Uzzi Ornan | Alon Itai
Computational Linguistics, Volume 21, Number 3, September 1995


Acquiring and Exploiting the User’s Knowledge in Guidance Interactions
Eyal Shifroni | Uzzi Ornan
Third Conference on Applied Natural Language Processing