A Study of Context Influences in Arabic-English Language Translation Technologies

Zailan Arabee Abdul Salam, Rabiah Abdul Kadir

Abstract


Social and cultural context is largely missing in current language translation systems. Dictionary based systems translate terms in a source language to an equivalent term in a target language, but often the translation could be inaccurate when context is not taken into consideration, or when an equivalent term in the target language does not exist. Domain knowledge and context can be made explicit by using ontologies, and ontology utilization would enable inclusion of semantic relations to other terms, leading to translation results which is more comprehensive than a single equivalent term. It is proposed that existing ontologies in the domain should be utilized and combined by ontology merging techniques, to leverage on existing resources to form a basis ontology with contextual representation, and this can be further enhanced by using machine translation techniques on existing corpora to improve the basic ontology to append further contextual information to the knowledge base. Statistical methods in machine translation could provide automated relevance determination of these existing resources which are machine readable, and aid the human translator in establishing a domain specific knowledge base for translation. Advancements in communication and technologies has made the world smaller where people of different regions and languages need to work together and interact. Machine translation provides an automatic approach to make translation facilities available to anyone, at any time. The accuracy of these translations are crucial as it could lead to misunderstandings and possible conflict. While single equivalent terms in a target language can provide a gist of the meaning of a source language term, a semantic conceptualisation provided by an ontology could enable the term to be understood in the specific context that it is being used.


Keywords


Machine Translation, Contextual Knowledge, Knowledge Representation

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e-ISSN : 2289-2192

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