Abstract

An approach to Example-Based Machine Translation is presented which operates by extracting and recombining translation patterns from a bilingual corpus aligned at the level of the sentence. The translation patterns are extracted using a recursive machinelearning algorithm based on the principle of similar distributions of strings: source and target language lexical items that co-occur in the same two sentence-pairs are likely to be translations of each other. The translation patterns extracted represent generalisations of sentences that are translations of each other in that certain sequences of words are replaced by variables. The translation patterns resemble, to a certain extent, transfer rules but with less constraints since there is no concept of syntactic structure in this approach: translation patterns are extracted based on the

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