Arabic/English word translation disambiguation using parallel corpora and matching schemes

This paper describes the implementation and evaluation of an Arabic/English word translation disambiguation approach that is based on exploiting a large bilingual corpus and statistical co-occurrence to find the correct sense for the query translations terms.

Abstract

Abstract. The limited coverage of available Arabic language lexicons causes a serious challenge in Arabic cross language information retrieval. Translation in cross language information retrieval consists of assigning one of the semantic representation terms in the target language to the intended query. Despite the problem of the completeness of the dictionary, we also face the problem of which one of the translations proposed by the dictionary for each query term should be included in the query translations. In this paper, we describe the implementation and evaluation of an Arabic/English word translation disambiguation approach that is based on exploiting a large bilingual corpus and statistical co-occurrence to find the correct sense for the query translations terms. The correct word translations of the given query term are determined based on their cohesion with words in the training corpus and a special similarity score measure. The specific properties of the Arabic language that frequently hinder the correct match are taken into account.