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| Multi-view Fusion DJ-TextRCNN for the Theme Recommendation of Ancient Texts |
| Wu Shuai1, Yang Xiuzhang2,3, He Lin1 |
1.College of Information Management, Nanjing Agricultural University, Nanjing 211800
2.School of Cyber Science and Engineering, Wuhan University, Wuhan 430072
3.School of Information, Guizhou University of Finance and Economics, Guiyang 550025 |
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Abstract Progress in digital humanities research is hindered by issues such as low working efficiency, blurred boundaries of cataloging topics, excessive reliance on expert knowledge, lack of in-depth mining of the semantics of ancient texts, and difficulty in accurately recommending topics in the field of ancient texts by combining the characteristics of ancient book texts. In this regard, this study aimed to realize the accurate recommendation of text theme content that satisfies the needs of researchers on the basis of the characteristics of ancient book corpora. First, ancient book corpus data annotated by the research group in the early stage were selected for subject category labeling and view classification. Second, a semantic mining model integrating a pretrained BERT model and improved convolutional neural network, recurrent neural network, and multi-head attention mechanism was constructed. Finally, the multi-view semantic enhancement model of “subject-relationship-object” was integrated to construct the DJ-TextRCNN model to realize more fine-grained, deeper, and multi-dimensional semantic mining of classic texts. The DJ-TextRCNN model achieved the best accuracy of ancient book theme recommendation tasks in different views. Under the “subject-relationship-object” view, an accuracy rate of 88.54% was reached, and the accurate theme recommendation of ancient texts was preliminarily realized. The model can help guide in-depth and fine-grained semantic mining of Chinese culture.
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Received: 11 April 2023
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