智能模糊决策树算法在英语机器翻译中的应用

Application of Intelligent Fuzzy Decision Tree Algorithm in English Machine Translation

自然语言处理是计算机科学中一种从人类语言中获取和分析含义并以智能方式与人类进行交互的方法;为解决短语匹配需要目标语言中的对应语言存在偏差的问题,提出了一种基于智能模糊决策树算法的英语分级机器翻译模型(HEMTM);模型通过搜索与分层英语机器翻译相关特征完成构建,同时,根据语言受欢迎程度和语义重要性对机器翻译的准确性进行排名,该模型在构建机器翻译的过程中,考虑了HEMTM与相应英语机器翻译支持关系之间的差异;研究结果显示,当采用HEMTM模型等级为CFGrank时,模型具有较高的准确性;在n=60,δ=0情况下,模型准确性为68%;该模型可应用于具有多个答案的英语机器翻译的构建,为英语机器翻译算法领域研究提供了参考。

Natural language processing(NLP)is a method of obtaining and analyzing meaning from human language and interacting with human in an intelligent way in computer science.In order to solve the problem that phrase matching needs the deviation of corresponding language in the target language,an English hierarchical machine translation model(HEMTM)based on intelligent fuzzy decision tree algorithm is proposed.In the process of building machine translation,the model takes into account the differences between HEMTM and the corresponding English machine translation support relationship.The results show that when the level of HEMTM model is CFG rank,the accuracy of the model is high;when n=60,δ=0,the accuracy of the model is 68%.The model can be applied to the construction of English machine translation with multiple answers,which provides a reference for the research of English machine translation algorithm.