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In this paper, we present an algorithm for extracting translations of any given multiword expression from parallel corpora. Given a multiword expression to be translated, the method involves extracting a short list of target candidate words from parallel corpora based on scores of normalized frequency, generating possible translations and filtering out common subsequences, and selecting the top-n possible translations using the Dice coefficient. Experiments show that our approach outperforms the word alignment-based and other naive association-based methods. We also demonstrate that adopting the extracted translations can significantly improve the performance of the Moses machine translation system.

Acquiring translation equivalences of multiword expressions by normalized correlation frequencies