摘要
如何根据源语言文本从大规模语料库中找出其最相近的翻译实例，即句子相似度计算，是基于实例翻译方法的关键问题之一。本文提出一种多层次句子相似度计算方法：首先基于句子的词表层特征和信息熵从大规模语料库中选择出少量候选实例，然后针对这些候选实例进行泛化匹配，从而计算出相似句子。在多策略机器翻译系统IHSMTS中的实验表明，当语料规模为20万英汉句对时，系统提取相似句子的召回率达96％。准确率达90％，充分说明了本文算法的有效性。
The retrieval of the similar translation examples corresponding to the SL sentence from the large-scale corpora, or the computation of sentence similarity, is one of the key problems of EBMT. A new multi-layer sentence similarity computation approach is proposed in this paper. First, a few candidate translation examples are selected form a large-scale corpus on the basis of the surface features and entropies of the given words. Second, the degree of generalization match between the input sentence and each of those candidate translation examples is computed respectively. Finally, the sentence similarity is computed according to the outcomes of the previous two steps. Experimental results from tests on IHSMTS show that this approach has a recall rate of 96% and a precision rate of 90% when applied to a corpus of 200,000 English-Chinese sentence pairs.
出处
《中文信息学报》
CSCD
北大核心
2006年第B03期47-52,共6页
Journal of Chinese Information Processing
基金
国家自然科学基金资助项目（60502048,60272088）
国家863计划资助项目（2002AA117010-02）
关键词
句子相似度
基于实例的机器翻译
多策略机器翻译
泛化匹配
sentence similarity
example-based machine translation
hybrid-strategy machine translation
generaliza-tion matching
