Bibliographic automatic classification algorithm based on semantic space transformation

Huafeng Yu

doi:10.1007/s11042-019-7400-3

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Bibliographic automatic classification algorithm based on semantic space transformation

2019-03-08

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Huafeng Yu *

DOI：10.1007/s11042-019-7400-3

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Abstract

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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.

Keywords:

Data mining

Chinese bibliographic classification

Semantic space transformation

Word frequency

inverse document frequency

Support vector machine

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