基于Logistic模型的商务英语文献翻译设备自动监测技术研究
|
摘要
为了提高商务英语文献自动翻译的准确性,进行商务英语文献翻译教学平台的优化设计,提出基于Logistic模型商务英语文献翻译教学平台设计方法,平台设计分为自动翻译算法设计和翻译平台的软件开发两大模块。采用Logistics模型进行商务英语文献翻译的语义特征分析,采用上下文特征匹配和自适应语义变量寻优方法进行商务英语文献翻译的自动化词汇特征分析,提取词汇与词汇之间的关联性特征量,在特定的商务背景下进行翻译的差异性修正,提高英语翻译的准确性。在Logistics模型下进行平台的软件设计,平台搭建主要分为词汇数据库模块、英语信息处理模块、网络接口模块和人机交互界面模块等。采用B/S构架协议进行翻译平台的集成开发设计。平台测试结果表明,采用该方法进行商务英语文献翻译的准确性较好,自动翻译能力较强。
In order to improve the accuracy of automatic translation of business English documents and optimize the design of translation teaching platform for business English documents,this paper puts forward a design method of business English document translation teaching platform based on Logistic model. The design of the platform is divided into two modules: the automatic translation algorithm design and the software development of the translation platform.The semantic features of business English literature translation are analyzed by using Logistics model. Context feature matching and adaptive semantic variable optimization are used to analyze the automatic lexical features in business English literature translation,and the correlation features between vocabulary and vocabulary are extracted. In order to improve the accuracy of English translation,the platform software is designed under the Logistics model. The platform is mainly divided into lexical database module,English information processing module,and so on. Network interface module and man-machine interface module,etc. The integrated development and design of translation platform are carried out by using the B/S framework protocol. The platform test results show that this method is more accurate in translation of business English documents. Strong automatic translation skills.
In order to improve the accuracy of automatic translation of business English documents and optimize the design of translation teaching platform for business English documents,this paper puts forward a design method of business English document translation teaching platform based on Logistic model. The design of the platform is divided into two modules: the automatic translation algorithm design and the software development of the translation platform.The semantic features of business English literature translation are analyzed by using Logistics model. Context feature matching and adaptive semantic variable optimization are used to analyze the automatic lexical features in business English literature translation,and the correlation features between vocabulary and vocabulary are extracted. In order to improve the accuracy of English translation,the platform software is designed under the Logistics model. The platform is mainly divided into lexical database module,English information processing module,and so on. Network interface module and man-machine interface module,etc. The integrated development and design of translation platform are carried out by using the B/S framework protocol. The platform test results show that this method is more accurate in translation of business English documents. Strong automatic translation skills.
引文
[1] LI Chenliang,SUN Aixin,ANWITAMAN D. TSDW:Twostage WSD using Wikipedia[J]. Journal of the American Society for Information Science and Technology,2013,64(6):1203-1223.
[2] Mahmoud E E. Complex complete synchronization of two nonidentical hyperchaotic complex nonlinear systems[J].Mathematical Methods in the Applied Sciences,2014,37(3):321-328.
[3] Palomares I,Martinez L,Herrera F. A consensus model to detect and manage non-cooperative behaviors in large scale group decision making[J]. IEEE Trans on Fuzzy System,2014,22(3):516-530.
[4]舒婷,刘泉,艾青松,等.基于梯形模糊数与二元语义需求权重确定方法[J].武汉理工大学学报,2011,33(12):111-114.
[5] ZHAO N,ZHANG S,YU R,et al. Exploiting interference for energy harvesting:a survey,research issues and challenges[J]. IEEE Access,2017(5):10403-10421.
[6] LI D,SHEN C,QIU Z. Two-way relay beamforming for sum-rate maximization and energy harvesting[C]. IEEE International Conference on Communications(ICC),2013:3115-3120.
[7]孟祥福,严丽,马宗民,等.基于语义相似度的数据库自适应查询松弛方法[J].计算机学报,2011,34(5):812-824.
[8]寇月,申德荣,李冬,等.一种基于语义及统计分析的Deep Web实体识别机制[J].软件学报,2008,19(2):194-208.
[9]覃雄派,王会举,李芙蓉,等.数据管理技术的新格局[J].软件学报,2013,24(2):175-197.
[10]刘冬宁,汤庸,滕少华,等.基于时态数据库的极小子结构逻辑系统[J].计算机学报,2013,36(8):1592-1601.
[11]林华山,刘洋,焦文品.自适应软件的策略自动生成与演化[J].计算机科学,2017,44(11):9-14.
[12] BI S,HO C K,ZHANG R. Wireless powered communication:opportunities and challenges[J]. IEEE Communications Magazine,2015,53(4):117-125.
[13] ULUKUS S,YENER A,ERKIP E,et al. Energy harvesting wireless communications:a review of recent advances[J].IEEE Journal on Selected Areas in Communications,2015,33(3):360-381.
[14] LIU Y. Joint resource allocation in SWIPT-based multi-antenna decode-and-forward relay networks[J]. IEEE Transactions on Vehicular Technology,2017,66(10):9192-9200.
[15] HU S,DING Z,NI Q. Beamforming optimisation in energy harvesting cooperative full-duplex networks with self-energy recycling protocol[J]. IET Communications,2016,10(7):848-853.
[2] Mahmoud E E. Complex complete synchronization of two nonidentical hyperchaotic complex nonlinear systems[J].Mathematical Methods in the Applied Sciences,2014,37(3):321-328.
[3] Palomares I,Martinez L,Herrera F. A consensus model to detect and manage non-cooperative behaviors in large scale group decision making[J]. IEEE Trans on Fuzzy System,2014,22(3):516-530.
[4]舒婷,刘泉,艾青松,等.基于梯形模糊数与二元语义需求权重确定方法[J].武汉理工大学学报,2011,33(12):111-114.
[5] ZHAO N,ZHANG S,YU R,et al. Exploiting interference for energy harvesting:a survey,research issues and challenges[J]. IEEE Access,2017(5):10403-10421.
[6] LI D,SHEN C,QIU Z. Two-way relay beamforming for sum-rate maximization and energy harvesting[C]. IEEE International Conference on Communications(ICC),2013:3115-3120.
[7]孟祥福,严丽,马宗民,等.基于语义相似度的数据库自适应查询松弛方法[J].计算机学报,2011,34(5):812-824.
[8]寇月,申德荣,李冬,等.一种基于语义及统计分析的Deep Web实体识别机制[J].软件学报,2008,19(2):194-208.
[9]覃雄派,王会举,李芙蓉,等.数据管理技术的新格局[J].软件学报,2013,24(2):175-197.
[10]刘冬宁,汤庸,滕少华,等.基于时态数据库的极小子结构逻辑系统[J].计算机学报,2013,36(8):1592-1601.
[11]林华山,刘洋,焦文品.自适应软件的策略自动生成与演化[J].计算机科学,2017,44(11):9-14.
[12] BI S,HO C K,ZHANG R. Wireless powered communication:opportunities and challenges[J]. IEEE Communications Magazine,2015,53(4):117-125.
[13] ULUKUS S,YENER A,ERKIP E,et al. Energy harvesting wireless communications:a review of recent advances[J].IEEE Journal on Selected Areas in Communications,2015,33(3):360-381.
[14] LIU Y. Joint resource allocation in SWIPT-based multi-antenna decode-and-forward relay networks[J]. IEEE Transactions on Vehicular Technology,2017,66(10):9192-9200.
[15] HU S,DING Z,NI Q. Beamforming optimisation in energy harvesting cooperative full-duplex networks with self-energy recycling protocol[J]. IET Communications,2016,10(7):848-853.