节点文献
基于神经网络的关系词非充盈态复句层次的自动识别
Hierarchy Division of Compound Sentence with Non-saturated Relation Word via Neural Network
【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%。
【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 word2 vec 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; Hierarchical division of compound sentence; Depen-dency grammar; word2vec; Neural network;
- 【文献出处】 计算机科学 ,Computer Science , 编辑部邮箱 ,2019年S2期
- 【分类号】TP391.1;TP183
- 【被引频次】3
- 【下载频次】114