Abstract：In recent years, the phrase-based statistical machine translation model has obtained more attention for its good translation performance. However, the model uses the strategy of precise matching in decoding, and the data sparseness becomes a serious problem. On the one hand, some phrases become the “unknown phrases” because they cannot be matched precisely in the phrase table; On the other hand, most of the phrases in the phrase table can’t be used in the translation process. Therefore, we propose a novel translation approach based on phrase fuzzy matching and sentence expansion. In our approach, for a phrase out of the phrase table, i.e. unknown phrase, we find its similar phrase in the phrase table through fuzzy matching. Then the sentence is expanded by replacing the original phrase with the similar ones before being translated into the target language. Finally, a combination of multi-classifier is employed to select the best translation. The experiment results show that this approach significantly improves the translation quality. Key wordsartificial intelligence; machine translation; phrase-based statistical machine translation; fuzzy matching; combination classifier
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2009, Vol. 23 
