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In this article we present a novel linguistically driven evaluation method and apply it to the main approaches of Machine Translation (Rule-based, Phrase-based, Neural) to gain insights into their strengths and weaknesses in much more detail than provided by current evaluation schemes. Translating between two languages requires substantial modelling of knowledge about the two languages, about translation, and about the world. Using English-German IT-domain translation as a case-study, we also enhance the Phrase-based system by exploiting parallel treebanks for syntax-aware phrase extraction and by interfacing with Linked Open Data (LOD) for extracting named entity translations in a post decoding framework.

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. Development and calibration of route choice utility models: neuro-fuzzy approach. Journal of Transportation Engineering 130 (2), 171-182. 11. YIN, H., WONG, S.C., XU, J., WONG, C.K., 2002. Urban traffi c fl ow prediction using a fuzzy-neural approach. Transportation Research Part C 10 (2), 85-98. 12. FANG, C., ELEFTERIADOU, L., PECHEUX, K.K., PIETRUCHA, M.T., 2003. Using fuzzy clustering of user perception to defi ne levels of service at signalized intersections. Journal of Transportation Engineering 129 (6), 657-663. 13. ZHANG, J.S., LEUNG, Y.W., 2004. Improved

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