Improving Machine Translation Using Hybrid Dictionary-Graph Based Word Sense Disambiguation with Semantic and Statistical Methods

Abstract

This paper describes a machine translation prototype in which noun phrase translation is defined as a subtask of machine translation. A dedicated noun phrase translation subsystem is built and improved to translate Arabic noun phrase into English using only minimal resources for both the source and the target language. This work proposes a dictionary-graph based WSD approach to improve machine translation using hybrid semantic-statistical method based on computing words relatedness and a statistical measure of association to get the relation between ambiguous words. This relation was used with viterbi search algorithm to find the appropriate translation of the Arabic noun phrase. A shallow source language analysis, combined with a translation dictionary and a mapping system of source language phenomena into the target language and a target language corpus for generation are all the resources needed in the described system.