Arabic to English Machine Translation of Verb Phrases Using Rule-Based Approach
Zainab Abd Algani and Nazlia Omar
DOI : 10.3844/jcssp.2012.277.286
Journal of Computer Science
Volume 8, Issue 3
Problem statement: Scientific translation represents an important stream in the current century due to explosion of the information revolution. The translation of scientific text is still limited in accuracy due to the fact that the scientific terms cannot be translated appropriately. Word order rules are very important for the generation of sentences in the target language whereas the word order in Arabic language is different from the order in English. Any Arabic Machine Translation (MT) system to English should be able to deal with word order. Approach: The aim of this study is to introduce-MT (Verbal Sentence rule based Machine Translation), an automatic system for Arabic verbal sentence of scientific text to English translation using transfer based approach. Verbal sentences constitute the majority of Arabic scientific documents. The system involves three phases: analysis, transfer and a generation phase. The transfer method is one of the rule based approach category and the most common technique used in machine translation system. Results: The system was trained on 45 verbal sentences from different Arabic scientific text and tested on 30 new verbal sentences from different domains. An experiment performed involves comparison with two other machine translation systems namely Syzran and Google. The accuracy of the result of the designed system is 93%. Conclusion: VS-MT has been successfully implemented and tested on many verbal sentences from different field of Arabic thesis. An experiment was performed which involves comparison with two other machine translation systems namely Syzran and Google. Our approach is efficient enough to translate Arabic verbal sentences of scientific text to English.
© 2012 Zainab Abd Algani and Nazlia Omar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.