Knowledge-based disambiguation for machine translation

Corpus-based Disambiguation for Machine Translation

This paper deals with problem of choosing a proper translation for polysemous words. We describe an original method for partial word sense disambiguation of such words using word sketches extracted from large-scale corpora and using simple English-Czech dictionary. Each word is translated from English to Czech and a word sketch for the word is compared with all word sketches of its appropriate ...

Kannada Word Sense Disambiguation for Machine Translation

Kannada Word Sense Disambiguation for Machine Translation

Polysemous Words can have more than one distinct meaning. Word sense disambiguation (WSD) is the ability to identify the exact meaning of such polysemous words in context in a computational manner. WSD is considered as an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problem in Artificial Intelligence. In this paper, we propose an Integrated Kanna...

On Knowledge-Based Machine Translation

This paper describes the design of tile knowledge representation medium used for representing concepts and assertions, respectively, in a subworld chosen for a knowledge-based machine u'anslation system. This design is used in the TRANSLATOR machine translation project. The kuowledge representation language, or interlingua, has two components, DIL and TIL. DIL stands for 'dictionary of interlin...

Word Sense Disambiguation for Statistical Machine Translation

While much effort has been put in designing and evaluating Word Sense Disambiguation (WSD) models for translation in the WSD community, standard Statistical Machine Translation (SMT) systems have achieved remarkable improvements in translation quality without modeling WSD explicitly. However, inspecting SMT output suggests that SMT needs better semantic modeling to accurately translate meaning....