A Hybrid Model for Enhancing Lexical Statistical Machine Translation
The interest in statistical machine translation systems increases currently due to political and social events in the world. A proposed Statistical Machine Translation (SMT) based model that can be used to translate a sentence from the source Language (English) to the target language (Arabic) automatically through efficiently incorporating different statistical and Natural Language Processing (NLP) models such as language model, alignment model, phrase based model, reordering model, and translation model. These models are combined to enhance the performance of statistical machine translation (SMT). Many implementation tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and BLEU. Based on the implementation, evaluation of this model, and comparing the generated translation with other implemented machine translation systems like Google Translate, it was proved that this proposed model has enhanced the results of the statistical machine translation, and forms a reliable and efficient model in this field of research.
Keywords: Machine Learning, Statistical Machine Translation, Lexical Machine Translation, Linguistics
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ABOUT THE AUTHORS
Ahmed G. M. El Sayed
Ahmed has a B.Sc. in Computer and Information Systems from Sadat Academy for Management Sciences of Cairo, Egypt (2007). Ahmed is a software quality control engineer with extensive experience and management skills and works for a hi-tech telecommunication company that develops and sells solutions that enable service providers to efficiently deliver high quality voice and data services over broadband access networks. As a senior software quality control engineer he is responsible for the quality software products produced by the company.
Ahmed S. Salama
received his B.Sc. degree in computer sciences and information systems from Sadat Academy in 1991 Egypt, M.Sc. in Information Technology from Alexandria University in 1998, and his Ph.D. In information technology in 2004 from Alexandria University Egypt, He is mainly interested in machine learning applications, and knowledge discovery in Bid Data. He worked as an assistant professor in information systems department in faculty of computer sciences – Taiba University – KSA, and now is working as an assistant professor in computer and information systems department in Sadat Academy.
Alaa El Din M. El Ghazali
received his Ph.D. in accounting from Suez Canal University Egypt, in 1993. He worked as a president of Sadat Academy for Management Sciences and now is working as a professor in computer and information systems department in Sadat Academy.
Ahmed G. M. El Sayed
Ahmed has a B.Sc. in Computer and Information Systems from Sadat Academy for Management Sciences of Cairo, Egypt (2007). Ahmed is a software quality control engineer with extensive experience and management skills and works for a hi-tech telecommunication company that develops and sells solutions that enable service providers to efficiently deliver high quality voice and data services over broadband access networks. As a senior software quality control engineer he is responsible for the quality software products produced by the company.
Ahmed S. Salama
received his B.Sc. degree in computer sciences and information systems from Sadat Academy in 1991 Egypt, M.Sc. in Information Technology from Alexandria University in 1998, and his Ph.D. In information technology in 2004 from Alexandria University Egypt, He is mainly interested in machine learning applications, and knowledge discovery in Bid Data. He worked as an assistant professor in information systems department in faculty of computer sciences – Taiba University – KSA, and now is working as an assistant professor in computer and information systems department in Sadat Academy.
Alaa El Din M. El Ghazali
received his Ph.D. in accounting from Suez Canal University Egypt, in 1993. He worked as a president of Sadat Academy for Management Sciences and now is working as a professor in computer and information systems department in Sadat Academy.
