机器英语翻译中的模糊语义最优解选取方法

Optimal solution selection from fuzzy semantics in machine translation of English

针对传统最优解选择过程中使用的多目标粒子群选择方法,具有极大的局限性,最优解选择贴合性较低问题,提出针对机器英语翻译中的模糊语义最优解选取方法。建立机器英语翻译的语义模型,对机器英语翻译自然语言实现连接处理,使用模型对连接处理后的语义本体进行映射分析,翻译选定过程使用英语翻译的语义相似度计算提升语义连贯性。实验结果表明,改进选取方法选定翻译英语词汇特征匹配度较高,并且贴合性强,更适用于机器英语翻译中最优解选择。

As the method of multi-objective particle swarm selection is used in the process of the traditional optimal solution selection,which has great limitations and low fitness to the optimal solution,an optimal solution selection method for fuzzy semantic in English machine translation is puts forward. A semantic model of English machine translation is built. The connection processing for natural language in machine translation of English was achieved. The model is used for mapping analysis of the semantic ontology after the connection treatment. The semantic similarity computation is adopted for English translation in translation selection process to enhance semantic coherence. The experimental results show that the improved selection method has high feature matching rate for the translation of English words and high fitness,and is more suitable for the optimal solution selection in English machine translation.