Journal of Computer Science

Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach

Omar Shirko, Nazlia Omar, Haslina Arshad and Mohammed Albared

DOI : 10.3844/jcssp.2010.350.356

Journal of Computer Science

Volume 6, Issue 3

Pages 350-356


Problem statement: Any Arabic to English Machine Translation (MT) system should be capable of dealing with word order which Arabic exhibits. This poses a significant challenge to MT due to the vast number of ways to express the same sentence in Arabic. The ordering features are very important and should be carefully applied to ensure the generation of sentence in the target language. Because they apply to the target language, it should fulfill the specific requirement of this language. Mistakes in the MT output can be either the result of analysis problems at the source language level, or due to generation problem at target language level. Word order rules are crucial for the generation of sentences in the target language. They also serve as rules for the ordering of sentence constituents. These rules draw their information from the syntactic knowledge. The word order problem becomes more obvious when making machine translation between languages that have rich morphological variations. Approach: The main objective of this research is to develop a machine translation that translates Arabic noun phrases into English by using transfer-based approach. A system called Npae-Rbmt has been developed in this research. Transfer-based machine translation is one instance of rule-based machine-translation approaches and is currently one of the most widely used methods of machine translation. The idea of transfer-based machine translation it is necessary to have an intermediate representation that captures the “meaning” of the original sentence in order to generate the correct translation. Using advantages of transfer-based machine translation such as analysis step, the Transfer-based becomes simpler as linguistic analysis goes deeper-as the representation of analysis step becomes more abstract. In fact, a major goal of MT research is to define a level of analysis which is so deep in which transfer-based machine translation is able to do. Results: The method was tested on 88 thesis titles and journals from the computer science domain. The accuracy of the result was 94.6%. These results proved the viability of this approach for distant languages. Conclusion: Based on the achieved results, we have managed to perform the syntactic reordering within an Arabic noun phrases to English translation task by using transfer-based machine translation and also achieved reasonable improvements in translation quality over related approach.


© 2010 Omar Shirko, Nazlia Omar, Haslina Arshad and Mohammed Albared. 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.