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  2. Master Thesis / Tesis Master
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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476142
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dc.contributor.advisorNazlia Omar, Dr.
dc.contributor.authorZainab Abd AlGani AlSaied (P50138)
dc.date.accessioned2023-10-06T09:14:00Z-
dc.date.available2023-10-06T09:14:00Z-
dc.date.issued2011-05-06
dc.identifier.otherukmvital:74584
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/476142-
dc.descriptionMachine translation (MT) refers to translation without human assistance which uses computer programs to translate from single language (Source Language (SL)) to another (Target Language (TL)). Machine translation is not only replacement of word by word, but it involves the function of multiple linguistic rules particularly in morphology, syntax and semantics. Machine Translation of Arabic language is much needed in Natural Language processing (NLP) community due to the growing number of users on the Internet and the propagation of communication. This work introduces VS-MT (Verbal Sentence rule based Machine Translation), an automatic system for Arabic verbal sentence of scientific text to English translation. Verbal sentences constitute the majority of Arabic scientific document. VS-MT is developed on a rule-based approach. The system involves three phases: analysis, transfer and generation phase. The rule is applied in each stage of the process from the input text until the output sentence is generated. The analysis phase contains Arabic monolingual dictionary, Arabic morphological analyzer and Arabic verbal sentence parsing. In the transfer phase, the rules are applied to convert Arabic verbal sentence words to an equivalent English meaning and Arabic verbal sentence pattern to an equivalent English pattern. In the generation phase, a set of transformational grammar is applied to synthesize the translation in the target language to get a grammatically correct structure. 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 Systran and Google. The accuracy of the result of the designed system is 93 %.This shows that our approach is efficient enough to translate Arabic verbal sentences of scientific text to English.,Master/Sarjana
dc.language.isoeng
dc.publisherUKM, Bangi
dc.relationFaculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat
dc.rightsUKM
dc.subjectArabic
dc.subjectEnglish
dc.subjectMachine translation
dc.subjectVerb phrases
dc.subjectRule based approach
dc.subjectMachine translating
dc.titleArabic to english machine translation of verb phrases using rule based approach
dc.typetheses
dc.format.pages92
dc.identifier.callnoQA76.6.S235 2011
dc.identifier.barcode000775
Appears in Collections:Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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