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

Extracting Value Elements and Constructing Index System for Calligraphy Works Based on Hyperplane-BERT-Louvain Optimized LDA Model

【目的】针对书法作品价值评估分歧大、标准缺失的难题,借助大数据与人工智能方法高效、准确地识别书法作品价值要素,为各种书法作品交易活动提供技术支撑。【方法】首先,融合超平面算法和BERT模型对书法文献进行停用词剔除与语义扩充,形成高辨识度的优化语料库;其次,构建书法文献复杂语义网络,引入Louvain算法,通过最大化社区网络的模块度确定最优主题数;最终,本文提出一种基于超平面-BERTLouvain-LDA(HBL-LDA)的新方法来高效、准确地构建书法价值评估指标体系。【结果】相比于LDA,HBLLDA模型的主题识别查准率和F值分别提高了45.00个百分点和29.46个百分点,平均主题优质率减少了0.96,识别的优质主题更多。基于代表性书法作品,利用多种回归模型对评估指标体系进行验证,准确率高达84.00%。【局限】只针对书法作品构建了评估指标体系,难以在其他艺术品数据上适配。而且,BERT模型缺乏主题语义信息,使得相似特征词扩充具有一定的局限性。【结论】本文提出一种基于超平面-BERTLouvain组合优化LDA模型,构建书法价值评估指标体系的新模式,为其他领域指标体系的构建提供了新方向。

[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.