一种有效的基于Web的双语翻译对获取方法

命名实体和新词、术语的翻译对机器翻译、跨语言检索、自动问答等系统的性能有着重要的影响,但是这些翻译很难从现有的翻译词典中获得。该文提出了一种从中文网页中自动获取高质量双语翻译对的方法。该方法利用网页中双语翻译对的特点,使用统计判别模型,融合多种识别特征自动挖掘网站中存在的双语翻译对。实验结果表明,采用该模型构建的双语翻译词表,TOP1的正确率达到82.1%,TOP3的正确率达到94.5%。文中还提出了一种利用搜索引擎验证候选翻译的方法,经过验证,TOP1的正确率可以提高到84.3%。

An Effective Method to Extract Translation Pairs from Web Corpora

The translations of named entities,out of vocabulary words and terms play an important role in many application systems such as machine translation,cross-language information retrieval and question answer.However,these translations are hard to access from traditional bilingual dictionary.This paper proposes a method to automatically extract high quality translation pairs from Chinese web corpora.It analyzes the features of bilingual translation pairs in web pages,and then a statistical discriminative model combined with multiple features is used to extract translation pairs.Experimental results show that the quality of the extracted bilingual translations is improved greatly: Top1 accuracy 82.1%,and Top3 94.5%.The paper also proposes a verification method to further improve the accuracy of the initial extractions with the help of search engines.Top1 accuracy grows up to 84.3% after the verification.