Extracting Value Elements and Constructing Index System for Calligraphy Works Based on Hyperplane-BERT-Louvain Optimized LDA Model
Pan Xiaoyu1,Ni Yuan2,3(),Jin Chunhua2,Zhang Jian2,3
1Computer School, Beijing Information Science & Technology University,Beijing 100192, China 2School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China 3Beijing Key Laboratory of Big Data Decision Making for Green Development, Beijing 100192, China
[Objective] This paper uses big data and artificial intelligence to identify the value elements of calligraphy works and provides technical support for their trading activities. It addresses the issue of lacking standards in the assessment of calligraphy works. [Methods] First, we combined the hyperplane algorithm and BERT model to preprocess calligraphy documents by eliminating stop words and expanding semantics to create an optimized corpus with high recognition. Secondly, we constructed a complex semantic network for calligraphy literature and introduced the Louvain algorithm to determine the optimal number of topics by maximizing the modularity of the community network. Finally, we developed a new method based on “Hyperplane-Bert-Louvain-LDA” (HBL-LDA) to construct an assessment index system of calligraphy value. [Results] Compared with LDA, the precision and F value of the topic recognition of the HBL-LDA were increased by 45.00% and 29.46%, respectively. The average topic quality rate was reduced by 0.96, with more high-quality topics identified. We also used regression models to verify the evaluation index system with representative calligraphy works, with the highest accuracy rate of 84.00%. [Limitations] This paper only constructed an evaluation system for calligraphy works, which cannot be applied to other artworks. The BERT model lacks the topic semantic information, which makes it challenging to expand similar feature words. [Conclusions] The new model for calligraphy value evaluation proposed in this paper provides new directions for constructing index systems in other fields.
潘小宇, 倪渊, 金春华, 张健. 基于超平面-BERT-Louvain优化LDA模型的书法作品价值要素提取及指标体系构建*[J]. 数据分析与知识发现, 2023, 7(10): 109-118.
Pan Xiaoyu, Ni Yuan, Jin Chunhua, Zhang Jian. Extracting Value Elements and Constructing Index System for Calligraphy Works Based on Hyperplane-BERT-Louvain Optimized LDA Model. Data Analysis and Knowledge Discovery, 2023, 7(10): 109-118.
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