Word Sense Disambiguation Using Target Language Corpus in a Machine Translation System
Tayebeh Mosavi Miangah, Ali Delavar Khalafi · Digital Scholarship in the Humanities · 2005
This article studies different aspects of a new approach to word sense disambiguation using statistical information gained from a monolingual corpus of the target language. Here, the source language is English and the target is Persian, and the disambiguation method can be directly applied in the system of English-to-Persian machine translation for solving lexical ambiguity problems in this system. Unlike other disambiguation approaches, using corpora for handling the problem, which use the Most Likelihood Model in their statistical works, this article proposes the Random Numbers Model. We believe that this model is more reasonable from the scientific point of view and find that it offers the most precise and accurate results. This method has been tested for a selected set of English texts containing multiple-meaning words with respect to Persian language and the results are encouraging.