Concept extraction for structured text using entropy weight method
Jie Yu, Rongrong Chen, Lingyu Xu, Dongdong Wang · 2019
With the rapid development of computer and communication, data mining and related fields are facing huge challenges. In the research of data mining and text analysis, how to extract concepts more objectively and reasonably is a key issue. Existing approaches seem to lack the intensive analysis of structural nature of structured text, which will affect the effectiveness and accuracy of text analysis. This paper proposes a novel approach of structured text analysis and concept extraction using entropy weight method. It quantitatively assesses the contribution of each module in evaluating the weight of concept. Based on the position of the concept in the text, concepts can be mined more effectively. Furthermore, the method is applied to the field of academic text analysis to verify its effectiveness and advantages over traditional methods. And the experimental results also prove that the method can provide solid theoretical and technical support for personalized academic information services