基于PSO-ELM的外宣翻译文本语料识别研究  

Research on corpus recognition of publicity translation text based on PSO-ELM

作　　者：胡琰琪[1] 李艳[1] Hu Yanqi;Li Yan(Shangluo College,Shangluo,Shanxi 726000)

机构地区：[1]商洛学院,陕西商洛726000

出　　处：《现代科学仪器》2022年第2期194-197,共4页Modern Scientific Instruments

基　　金：陕西省教育厅课题项目(编号:18JK0252);商洛市科技局项目(编号:2020-C-0023)。

摘　　要：外宣文本的翻译准确性直接影响了我国的国际形象,特别是在国际社会对中国声音关注度的提升,外宣文本翻译更为重要。基于此,本文采用PSO-ELM算法建立了外宣翻译文本语料识别系统,即采用语料库样本对PSO-ELM模型进行训练和测试,对实际的外宣文本进行语料提取并借助语料识别系统进行语料识别。将提出的方法应用于具体的外宣翻译文本语料识别中,结果表明,PSO-ELM模型对各种外宣翻译文本语料识别的精度远高于ELM模型。该研究对提升外宣翻译文本语料识别精度,在国际社会有效传播中国声音具有一定的现实意义。The accuracy of translation of the publicity text directly affects the international image of our country,especially in the international community to the improvement of the voice of China,the translation of the publicity text is more important.Based on this,this paper uses PSO-ELM algorithm to establish a corpus recognition system for the translation of publicity,that is,the PSO-ELM model is trained and tested by corpus samples,and the actual publicity text is extracted and corpus recognition is carried out by the corpus recognition system.The results show that the PSO-ELM model has a higher accuracy than the ELM model in the recognition of various kinds of publicity translation texts.This study is of practical significance to improve the recognition accuracy of the translated texts and to effectively disseminate Chinese voice in the international community.

关 键 词：PSO-ELM算法 外宣翻译 语料识别 

分 类 号：H315.9[语言文字—英语]