Statistical machine translation method based on improved neural network

被引:0
作者
Yang, Lingxing [1 ]
机构
[1] Qujing Normal Univ, Coll Phyis & Elect Engn, Qujing, Yunnan, Peoples R China
来源
AGRO FOOD INDUSTRY HI-TECH | 2017年 / 28卷 / 01期
关键词
Improved neural network; statistical machine translation; SUPPORT VECTOR MACHINE; CLASSIFICATION;
D O I
暂无
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
With the development of science and technology and the deepening of globalization, the computer technology begins to permeate all aspects of people's life, and especially the method of statistical machine translation has made great achievements in recent years. In view of this, the method of statistical machine translation based on improved neural network in this paper was researched, then, through the improved neural network structure model, the pre-scheduling model and the corpus analysis method, the translation method of the statistical machine were analyzed and studied. The experiments show that the pre-scheduling model based on the improved neural network makes the translation results of statistical machines more accurate, which is conducive to promote the world information exchange.
引用
收藏
页码:1715 / 1719
页数:5
相关论文
共 16 条
[1]   Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye [J].
Asfaram, A. ;
Ghaedi, M. ;
Azqhandi, M. H. Ahmadi ;
Goudarzi, A. ;
Dastkhoon, M. .
RSC ADVANCES, 2016, 6 (46) :40502-40516
[2]  
Bahdanau D, 2014, ARXIV14090473, V2, P421
[3]   Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation [J].
Boukra, Tahar ;
Lebaroud, Abdesselam ;
Clerc, Guy .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2013, 60 (09) :4034-4042
[4]  
Cho K, 2014, ARXIV14061078, V3, P14
[5]  
Cho K., 2014, 8 WORKSH SYNT SEM ST, P1
[6]   Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition [J].
Dahl, George E. ;
Yu, Dong ;
Deng, Li ;
Acero, Alex .
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2012, 20 (01) :30-42
[7]   Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree [J].
Dieu Tien Bui ;
Tran Anh Tuan ;
Klempe, Harald ;
Pradhan, Biswajeet ;
Revhaug, Inge .
LANDSLIDES, 2016, 13 (02) :361-378
[8]   Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines [J].
Kaytez, Fazil ;
Taplamacioglu, M. Cengiz ;
Cam, Ertugrul ;
Hardalac, Firat .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2015, 67 :431-438
[9]  
Meng F, 2015, ARXIV150301838, V8, P45
[10]  
Mikolov T, 2013, ARXIV13094168, V2, P45