A Sentence Alignment Model Based on Combined Clues and Kernel Extensional Matrix Matching Method

A sentence alignment model based on combined clues and Kernel Extensional Matrix Matching (KEMM) method is proposed. In this model, a similarity matrix for sentence aligning is formed by the similarities of bilingual sentences calculated by the combined clues, such as lexicon, morphology, length and special symbols, etc.; then this similarity matrix is used to construct a select matrix for sentence aligning; finally, obtains the sentence alignments by KEMM. Experimental results illustrated that our model outperforms over the Gale's system on handling any types of sentence alignments, with 30% total sentence alignment error rate decreasing.