Keywords extraction method based on two-way feature fusion

In order to improve the accuracy of keyword extraction, an improved method was proposed to solve the problem of missing keywords in traditional TF-IDF keyword extraction algorithm. In this method, two routes are used to extract the features of the text, one is responsible for storing the dependency syntactic relations in the preprocessing of the text, and the other is responsible for calculating the tF-IDF value, part of speech and position of the words, and constructing the comprehensive scoring weighting formula of the candidate keywords. Finally, the feature information of these two paths is fused in the stage of combinatorial optimization and sorted to form formal extracted keywords. Experimental results show that compared with the traditional TF-IDF algorithm and Textrank algorithm, the F value of the proposed method in Sina news data set is improved by 14.4% and 16.3% respectively, which effectively verifies that the proposed method can significantly improve the keyword extraction performance.