【摘要】 词对齐技术在机器翻译,特别是在统计机器翻译中起着重要作用。词形、语义、句法的多样性和灵活性,未登陆词及分词错误等不利因素,直接或间接影响了词对齐的质量。多策略英汉词对齐方法,融合了基于词典、GIZA++以及基于知网的词对齐方法。通过对双语语料和多策略对齐结果的分析,利用集合形式的运算指导词对齐的消歧过程。实验结果表明,该方法在对齐结果上F值较IBM模型提高近10%,达到了85.07%,对齐错误率降低10%。该方法根据不同算法对齐结果的可靠性和相容性,实现了各种算法的优势互补。更多还原

【Abstract】 Aligning bilingual corpus at the word level is very important to statistical machine translation (SMT). The diversity and feasibility of morphology, semantics and syntax, with out-of-vocabulary words and segmentation error directly or indirectly affect the word alignment. An efficient multi-strategy alignment algorithm is presented, by combining the lexical information, GIZA++ results and HowNet. A set form operation is used to guide the disambiguation process of word alignment, according to the analysis of the bilingual corpus and the alignment result. The experiments show that F-score is 85.07% and increased by 10% over optimized IBM model, and alignment error ratio is decreased by 10%. The strategy complements the advantages of those algorithms according to the reliability and consistence of them.更多还原