Affiliations: [a] School of Computer Science, Nanjing Audit University, Nanjing, China | [b] Jinken College of Technology, Nanjing, China | [c] School of Integrated Circuit, Wuxi Vocational College of Science and Technology, Wuxi, China
Correspondence:
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Corresponding author: Tingting Zhang, School of Computer Science, Nanjing Audit University, Nanjing 211815, China. E-mail: ttz1218@126.com.
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.
Keywords: Multi-view, embedding, semantic, feature extraction, link prediction
