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Bibliographic automatic classification algorithm based on semantic space transformation

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Abstract

In view of the Chinese bibliographic data mining application of Chinese bibliography, an improved semantic space transformation method is proposed. Firstly, the ICTCLAS system is used to preprocess the texts and construct lemma vectors based on word frequency features. Then, the frequency features of the word frequency and the frequency of the inverse frequency document are fused to construct the feature matrix of the training sample set. Then, the matrix is decomposed and transformed by the singular value to obtain a semantic space, which is for the goal of performing semantic space transformation on the text eigenvectors to obtain semantic vectors. Finally, a joint SVM classifier is constructed to automatically classify the semantic vectors corresponding to Chinese bibliography. Extensive experimental results show that the classification accuracy of this method is higher than the existing methods.

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Acknowledgments

This work has received financial support by The Teaching Reform Project of Higher Education of Zhejiang Province (No.jg20160374).

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  1. School of Digital Information Technology, Zhejiang Technical Institute of Economics, Hangzhou, 310018, China

    Hua-Feng Yu

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  1. Hua-Feng Yu
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Correspondence to Hua-Feng Yu.

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Yu, HF. Bibliographic automatic classification algorithm based on semantic space transformation. Multimed Tools Appl 79, 9283–9297 (2020). https://doi.org/10.1007/s11042-019-7400-3

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