Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 103-107.

• Intelligent Computing • Previous Articles     Next Articles

Hierarchy Division of Compound Sentence with Non-saturated Relation Word via Neural Network

YANG Jin-cai, YANG Lu-lu, WANG Yan-yan, SHEN Xian-jun   

  1. (School of Computer,Central China Normal University,Wuhan 430079,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: Hierarchical division of a compound sentence is the basis of syntactic structure analysis and semantic discrimination.However,the ellipsis of relational markers bring difficulties to the hierarchical division of a compound sentence.This paper combined dependency syntactic trees and word2vec word vector model to extract the syntactic structure and semantic features of compound sentences,then used the neural network to train a hierarchy division model for compound sentences with non-saturated relation word,and the hierarchical division test was carried out on the complex sentences in the test set.The test accuracy of test set is 74%.

Key words: Compound sentence with non-saturated relation word, Depen-dency grammar, Hierarchical division of compound sentence, Neural network, word2vec

CLC Number: 

  • TP751
[4]杜超华,胡金柱,沈威,等.基于复句语料库分词系统研究 [J].计算机数字与工程,2007,35(5):43-44.
[13]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]∥Advances in Neural Information Processing Systems.2013: 3111-3119.
[14]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[EB/OL].
[15]YU M,DREDZE M.Improving lexical embeddings with semantic knowledge[C]∥Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.Baltimore,Stroudsburg,USA:ACL,2014:545-550.
[16]KINGMA D P,JIMMY B A.Adam:A Method for StochasticOptimization[C]∥3rd International Conference for Learning Representations.San Diego,2015.
[17]TANG D Y,QIN B,LIU T.Document modeling with gated recurrent neural network for sentiment classification[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1422-1432.
[18]LAI S,XU L,LIU K,et al.Recurrent convolutional neural networks for text classification[C]∥Proceedings of the National Conference on Artifical Intelligence.2015:2267-2273.
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