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Bibliographic automatic classification algorithm based on semantic space transformation
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DOI：10.1007/s11042-019-7400-3.png)
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摘要
En 中文
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.
Keyword:
Data mining
Chinese bibliographic classification
Semantic space transformation
Word frequency
inverse document frequency
Support vector machine
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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