Construction of Semantic Fusion Model Oriented to Project Lifecycle
In the process of semantic fusion modeling,the fusion modeling effect is easily disturbed by redundant data.In order to solve the above problems,the semantic fusion modeling method is used in the construction of the project life cycle model.This modeling method firstly uses TF-IDF weighting algorithm to extract semantic feature vectors and input them to weighted naive Bayesian classifier.Based on the output semantic feature classification results,a semi supervised learning model based on graph model is constructed to realize the semantic feature fusion after classification and complete the construction of the semantic fusion model oriented to the whole life cycle of the project.The experimental results show that the proposed method has high fusion accuracy,high recall and high Rouge-L(Longest Common Subsequence index)value,and can accurately achieve semantic feature extrac-tion and model construction.
feature extractiondifference measure functioncomprehensive feature weight valueregularization operationvalue function
万方数据
