Intelligent Decision Technologies, volume 18, issue 3, pages 2421-2437

Structural link prediction model with multi-view text semantic feature extraction

Ke Chen 1
Tingting Zhang 1
Yuanxing Zhao 2
Taiyu Qian 3
2
 
Jinken College of Technology, Nanjing, China
3
 
School of Integrated Circuit, Wuxi Vocational College of Science and Technology, Wuxi, China
Publication typeJournal Article
Publication date2024-09-16
scimago Q3
SJR0.266
CiteScore1.7
Impact factor0.6
ISSN18724981, 18758843
Abstract

The exponential expansion of information has made text feature extraction based on simple semantic information insufficient for the multidimensional recognition of textual data. In this study, we construct a text semantic structure graph based on various perspectives and introduce weight coefficients and node clustering coefficients of co-occurrence granularity to enhance the link prediction model, in order to comprehensively capture the structural information of the text. Firstly, we jointly build the semantic structure graph based on three proposed perspectives (i.e., scene semantics, text weight, and graph structure), and propose a candidate keyword set in conjunction with an information probability retrieval model. Subsequently, we propose weight coefficients of co-occurrence granularity and node clustering coefficients to improve the link prediction model based on the semantic structure graph, enabling a more comprehensive acquisition of textual structural information. Experimental results demonstrate that our research method can reveal potential correlations and obtain more complete semantic structure information, while the WPAA evaluation index validates the effectiveness of our model.

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