基于神经网络的关系词非充盈态复句层次的自动识别

摘要/Abstract

摘要： 复句层次关系划分是复句句法结构分析以及语义甄别的基础,但关系词非充盈态复句由于关系标记的省略给层次划分带来了困难。文中利用依存关系句法树和word2vec词向量模型的方法来提取复句中分句的句法特征和语义特征,并利用神经网络进行训练,获得三句式关系词的非充盈态复句层次划分模型,对测试集中的复句进行层次划分测试,其准确率为74%。

关键词: word2vec, 复句层次划分, 关系词非充盈态, 神经网络, 依存句法

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

中图分类号:

TP751

杨进才, 杨璐璐, 汪燕燕, 沈显君. 基于神经网络的关系词非充盈态复句层次的自动识别[J]. 计算机科学, 2019, 46(11A): 103-107. https://doi.org/

YANG Jin-cai, YANG Lu-lu, WANG Yan-yan, SHEN Xian-jun. Hierarchy Division of Compound Sentence with Non-saturated Relation Word via Neural Network[J]. Computer Science, 2019, 46(11A): 103-107. https://doi.org/

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