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In this paper we describe a methodological analysis of EBMT (Example-Based Machine Translation) based on a CBR (Case-Based Reasoning) perspective. This analysis focuses on adaptation. We argue that, just as in CBR, the overall power of an EBMT system is its ability to adapt examples retrieved to suit the new problem translation. Here we describe a technique whereby reusability is a function of the abstract "adaptability" information stored in the cases. This information is exploited during both the adaptation and retrieval stages.

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