Lexical ambiguity resolution for turkish in direct transfer machine translation models

Authors:
A. Cüneyd Tantuğ;Eşref Adalı;Kemal Oflazer
Affiliations:
Computer Engineering Department, Istanbul Technical University Faculty of Eletrical-Electronic Engineering, Istanbul, Türkiye;Computer Engineering Department, Istanbul Technical University Faculty of Eletrical-Electronic Engineering, Istanbul, Türkiye;Faculty Of Engineering and Natural Sciences, Sabancı University, Tuzla, Türkiye
Venue:
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Year:
2006

Citing 6
Cited 0

An experiment in computational discrimination of English word senses

IBM Journal of Research and Development
Using multiple knowledge sources for word sense discrimination

Computational Linguistics
Automatic word sense discrimination

Computational Linguistics - Special issue on word sense disambiguation
Machine translation of very close languages

ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Unsupervised word sense disambiguation rivaling supervised methods

ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Corpus-based statistical sense resolution

HLT '93 Proceedings of the workshop on Human Language Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a statistical lexical ambiguity resolution method in direct transfer machine translation models in which the target language is Turkish. Since direct transfer MT models do not have full syntactic information, most of the lexical ambiguity resolution methods are not very helpful. Our disambiguation model is based on statistical language models. We have investigated the performances of some statistical language model types and parameters in lexical ambiguity resolution for our direct transfer MT system.