Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (20): 104-108.DOI: 10.3778/j.issn.1002-8331.2010-0061

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SVM Algorithm for N1+N2 Structure Syntax Relation Determination

YANG Quan   

  1. College of Chinese Language and Culture, Beijing Normal University, Beijing 100875, China
  • Online:2021-10-15 Published:2021-10-21



  1. 北京师范大学 汉语文化学院,北京 100875


Determining the grammatical relationship of phrase structures is a key problem in the field of Natural Language Processing(NLP). In order to apply Support Vector Machine(SVM) to classify and judge phrase structures, it needs to transform Chinese phrase structures into numerical vectors. On the basis of the self-built N1+N2 structure corpus, the semantic coding of two nouns in N1+N2 structure is carried out by using Cilin, and the coding is converted into numerical vector. Then, support vector machine is used to determine the grammatical relationship of the structure. Finally, random cross validation method is used to test the structure according to the ratio of training set to test set of 9∶1, and the average accuracy is 86.2%. The experimental results show the effectiveness of the proposed algorithm and the necessity of using artificial intelligence to deal with problems in the field of natural language processing.

Key words: natural language processing, artificial intelligence, support vector machine, phrase level, grammatical relation, ontology



关键词: 自然语言处理, 人工智能, 支持向量机, 短语层级, 语法关系, 知识本体