The paper presents an Expectation Maximization (EM) algorithm for automatic generation of parallel and quasi-parallel data from any degree of comparable corpora ranging from parallel to weakly comparable. Specifically, we address the problem of extracting related textual units (documents, paragraphs or sentences) relying on the hypothesis that, in a given corpus, certain pairs of translation equivalents are better indicators of a correct textual unit correspondence than other pairs of translation equivalents. We evaluate our method on mixed types of bilingual comparable corpora in six language pairs, obtaining state of the art accuracy figures.
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