Translation discovery using diverse similarity measures

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Translation discovery using diverse similarity measures "
Charles F. Schafer, III D. Yarowsky

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	54463
Doc. No	:	TL24417
Call number	:	‭3213794‬
Main Entry	:	Charles F. Schafer, III
Title & Author	:	Translation discovery using diverse similarity measures\ Charles F. Schafer, III
College	:	The Johns Hopkins University
Date	:	2006
Degree	:	Ph.D.
student score	:	2006
Page No	:	228
Abstract	:	Automatic translation between natural languages using empirical learning methods is an active research area. Much of the work in this area is concentrated on acquiring models of translation from expensive resources, including large databases containing hundreds of thousands of translated sentences, which are available for only a small number of the language pairs between which one might wish to translate. This dissertation describes the automatic acquisition of translations into English for unknown foreign language words given the lack of any high-quality translation learning resources, which is the reality for nearly all of the world's languages. The methods presented in this dissertation require minimal training supervision; the necessary inputs consist of only a few hundred thousand words of raw news text in a language of interest, such as Uzbek, and a translation dictionary into English for some related language, such as Turkish. We show how diverse measures of cross-language word similarity can be combined to learn translations for a considerable subset of the vocabulary of a language for which no translations are initially known. These similarities include statistical models of related-language cognate words, various cross-language comparisons of how words are used in monolingual text, and the distribution of terms in dated news articles. The ultimate result is a functional translation capability for new language pairs using minimal training resources and almost no human supervision.
Subject	:	Applied sciences; Machine translation; Natural language processing; Similarity; Translation; Computer science; 0984:Computer science
Added Entry	:	D. Yarowsky
Added Entry	:	The Johns Hopkins University