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Machine translation, a part of computational Linguistics, belongs to Natural Language Processing (NLP) and is a hot issue in the computational society. Gap between the linguist and the computer programmer, gives birth to so many problems like lexical ambiguity, syntactic and structural ambiguity, polysemy, induction, discourses, anaphoric ambiguity and different shade of meanings. Mostly English-to-Urdu machine translation systems were developed without considering the target language and also semantics are not included in existing systems. This alarming problem generates several issues during Natural Language Processing. We, in this paper, proposed and designed a new Knowledge Based Machine Translation System to overcome the above mentioned problems by using data mining and text mining techniques. Our machine translation system fulfills almost all the requirements of Natural Language Processing and ComputationalLinguistics. Basically this system is designed for Urdu but it can be used for many other languages. The proposed system will give better results as compared to existing systems.

From many years, machine translation and computational linguistic research community has given immense attention towards the development of machine translation techniques. In order to fulfill the goal of machine translation "translation without losing meaning", a lot of translation methods have been proposed. All of these translation methods differ in their theories and implementation strategies. Although some basic rules of translation are same but many of them vary with the selection of language pair. While concerning with the scientific text, every science domain has thousands of terminologies. Translation of these terminologies according to the domain boosts the performance of translation. Translation of scientific text is ignored in the literature, as it needs more effort and expertise of both domain and language are required. In this research, we have proposed an effective scientific text translator for English to Urdu to cope with the challenge of scientific text translation. This method tags and translate the terms according to the domain. We have introduced a term tagger for tagging terms. The system can work for any domain but for experimental purpose we have selected the domain of computer science. System is evaluated on selfgenerated corpus of computer science. It is also compared with the existing translators to demonstrate the dominance of proposed translator as compared to the competitor. The comparative results of proposed approach and existing are shown in the form of tables.

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