The number of sentence pairs in the bilingual corpus is a key to translation accuracy in computational machine translations. However, if the amount goes beyond a certain degree, the increasing number of cases has less impact on the translation while the construction of translation systems requires a considerable amount of time and energy, thus preventing the development of a statistical translation by the computer. This article offers a number of classifications for measuring the amount of information for each pair of sentences, using the Heuristic Bilingual Graph Corpus Network (HBGCN) to form an improved method of corpus selection that takes the difference between the first amount of information between the pairs of sentences into account. Using a graphic-based selector method as a training set, they achieve a close translation result through our experiments with the whole body and achieve better results than basic results for the following based on the Document Inverse Frequency (DIF) ranking approach.

中文翻译：

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基于统计翻译方法的启发式双语图语料网络改进英语教学方法

双语语料库中句子对的数量是计算机翻译中翻译准确性的关键。但是，如果数量超过一定程度，案件数量的增加对翻译的影响较小，而翻译系统的构建需要相当多的时间和精力，从而阻碍了计算机统计翻译的发展。这篇文章提供了一些分类来衡量每对句子的信息量，使用启发式双语图语料网络（HBGCN）形成了一种改进的语料选择方法，该方法取对之间的第一信息量之间的差异考虑到句子。使用基于图形的选择器方法作为训练集，