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于建平
职 称 : 教授
单 位 : 外国语学院
学历 :
硕士研究生毕业
出生年月 :
1957-10
毕业院校 :
上海外国语大学
论文成果
[1] 路懿.毛秉毅,于建平.Derivation and isomorphism identification of valid topological graphsfor 1-, 2-DOF planar closed mechanisms by characteristic strings.KSME.2011
[2] 于建平.付继林.A comparative study of word sense disambiguation of English modal verb by BP neural network and support vector machine..2011
[3] 路懿.胡波,于建平.Kinematics analysis of some linear legs with different structures for limited-DOF parallel manipulators.2011
[4] 李洪波.于建平.Attribute significance analysis of English modal verb shall in word sense disambiguation.2015
[5] 于建平.Solution of Semantic Mergers of English Modal Verbs.2013
[6] 于建平.Sense inference of english modal verb Must by adaptive network-based fuzzy inference system.2011
[7] 洪文学.于建平,宋佳霖.A new approach of generation of structured partial ordered attribute diagram based on covering.2015
[8] 于建平.A new approach of rules extraction for word sense disambiguation by features of attributes.2015
[9] 赵莎.于建平.Investigation of postgraduate learners awareness of genre features in academic writing by formal concept analysis.2015
[10] 李洪波.于建平.Knowledge Representation and Discovery for the Interaction between|Syntax and Semantics: a Case Study of Must.2014
[11] 于建平.Word sense disambiguation of English modal verb must by neural network.2010
[12] 于建平.研究生科技论文汉译英文化负迁移探析.2012
[13] 付继林.刘鸿宇,于建平.An investigation of influence of different subjective factors to WSD of English modal verb CAN.2015
[14] 于建平.付继林,白塔娜,洪文学.基于独有属性特征的情态与语境互动关系数据挖掘研究.2019
[15] 栾景民.宋佳霖,于建平,洪文学.THE CLASSIFICATION OF HSYES-ROTH DATASET BASED ON STRUCTURAL PARTIAL-ORDERED ATTRIBUTE DIAGRAM.2013
[16] 张淑媛.于建平.不同语言特征对英语助动词 May 在语义排歧中的贡献.2015
[17] 于建平.Word sense disambiguation of english modal verbs by support vector machines.2010
[18] 于建平.Word Sense Disambiguation of the English Modal Verb May by Back Propagation Neural Network.IEEE.2008
[19] 于建平.Interactive relations between semantic and syntactic features in word sense disambiguation of semantically complex words.2013
[20] 于建平.An investigation of contributions of different linguistic features to the WSD of english modal verb may by bp neural network.2009
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