卢蓉

• 1:海南大学

摘要(Abstract)：

针对传统基于规则的机器翻译模型存在英语翻译结果不够精确、难以准确描述词语间关系的弊端,设计并改进基于语义网络的英语机器翻译模型。该模型采用基于向量混合的短语合成语义统计英语机器翻译方法,在翻译相似度模型中,采用余弦相似度的方法获取两个向量的语义相似度,经过带权向量加法的计算极易辨别两个相似向量的不同之处,获取精准的英语翻译结果,对句子实施权值训练获取构成句子的主要短语,保证翻译结果归纳出句子的中心思想。改进基于语义网络的英语机器翻译模型,针对用户需求引入大数据的同时让语言学家参与到机器翻译的过程中,使得英语翻译结果既能独立进行语义表达,又能准确描述词语间关系。实验结果表明,所设计的模型能够精准高效地进行英语翻译。

关键词(KeyWords)： 语义网络;机器翻译;模型设计;语义相似度;语料库;权重训练

Abstract:

Keywords:

基金项目(Foundation): 海南省教育科学“十三五”规划课题(QJY201710118)~~

作者(Author): 卢蓉

DOI: 10.16652/j.issn.1004-373x.2018.14.031

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