Please wait a minute...
Data Analysis and Knowledge Discovery
Current Issue | Archive | Adv Search |
Extraction of Value Elements of Calligraphy Works and Construction of Index System Based on Hyperplane-Bert-Louvain Optimized LDA Model
Pan Xiaoyu,Ni Yuan,Jin Chunhua,Zhang Jian
(School of Computer Science, Beijing Information Science and Technology University, Beijing 100192, China) (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China) (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] Aiming at the problems of wide differences and lack of standards in the value evaluation of calligraphy works, this paper used big data and artificial intelligence methods to efficiently and accurately identify the value elements of calligraphy works and provide technical support for various calligraphy works trading activities.

[Methods] Combining hyperplane algorithm and Bert model to preprocess calligraphy documents by stop words screening and semantic expansion to form an optimized corpus with high recognition; Constructing complex semantic network of calligraphy literature and introducing Louvain algorithm to determine the optimal number of topics by maximizing the modularity of community network. Therefore, this paper put forward a new method based on "Hyperplane -Bert-Louvain-LDA" (HBL-LDA) to efficiently and accurately construct the evaluation index system of calligraphy value.

[Results] Experiments on calligraphy value literature showed that compared with the traditional LDA model, the precision and F value of the topic recognition of the HBL _ LDA model were increased by 45 % and 29.46 % respectively. What’s more, the average topic quality rate was less 0.96085, and more high-quality topics were identified. Finally, based on the representative calligraphy works, a variety of regression models were used to verify the evaluation index system, and the regression decision tree had the highest accuracy of 84 %.

[Limitations] The new model only constructed an evaluation index system for calligraphy works. In the future, it would incorporate multi-source data of other works of art into the construction of cultural product value indicators. Moreover, because the Bert model don’t consider the topic semantic information, the expansion of similar feature words had certain limitations.

[Conclusions] In this paper, a new model of calligraphy value evaluation index system based on "hyperplane -Bert-Louvain combination optimization LDA model" was proposed, which provided a new direction for the construction of index system in other fields. The index system constructed in this paper was easy to operate and adaptable, and could quickly generate a new index system as demand changes.

Key words Evaluation index system of calligraphy value      LDA      Field stop words      Louvain      Bert      
Published: 17 March 2023

Cite this article:

Pan Xiaoyu, Ni Yuan, Jin Chunhua, Zhang Jian. Extraction of Value Elements of Calligraphy Works and Construction of Index System Based on Hyperplane-Bert-Louvain Optimized LDA Model . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022-0915     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

[1] Lyu Xueqiang, Yang Yuting, Xiao Gang, Li Yuxian, You Xindong. Extracting Long Terms from Sparse Samples[J]. 数据分析与知识发现, 2024, 8(1): 135-145.
[2] He Chaocheng, Huang Qian, Li Xinru, Wang Chunying, Wu Jiang. Trending Topics on Metaverse: A Microblog Text Analysis with BERT and DTM[J]. 数据分析与知识发现, 2023, 7(9): 25-38.
[3] Zhao Xuefeng, Wu Delin, Wu Weiwei, Sun Zhuoluo, Hu Jinjin, Lian Ying, Shan Jiayu. Identifying High-Quality Technology Patents Based on Deep Learning and Multi-Category Polling Mechanism——Case Study of Patent Applications[J]. 数据分析与知识发现, 2023, 7(8): 30-45.
[4] Zhang Zhenqing, Sun Wei. Interdisciplinary Subject Recognition Based on Feature Measurement and PhraseLDA Model——Case Study of Nanotechnology in Agricultural Environment[J]. 数据分析与知识发现, 2023, 7(7): 32-45.
[5] Ben Yanyan, Pang Xueqin. Identifying Medical Named Entities with Word Information[J]. 数据分析与知识发现, 2023, 7(5): 123-132.
[6] Xu Kang, Yu Shengnan, Chen Lei, Wang Chuandong. Linguistic Knowledge-Enhanced Self-Supervised Graph Convolutional Network for Event Relation Extraction[J]. 数据分析与知识发现, 2023, 7(5): 92-104.
[7] Su Mingxing, Wu Houyue, Li Jian, Huang Ju, Zhang Shunxiang. AEMIA:Extracting Commodity Attributes Based on Multi-level Interactive Attention Mechanism[J]. 数据分析与知识发现, 2023, 7(2): 108-118.
[8] Zhao Yiming, Pan Pei, Mao Jin. Recognizing Intensity of Medical Query Intentions Based on Task Knowledge Fusion and Text Data Enhancement[J]. 数据分析与知识发现, 2023, 7(2): 38-47.
[9] Wang Yufei, Zhang Zhixiong, Zhao Yang, Zhang Mengting, Li Xuesi. Designing and Implementing Automatic Title Generation System for Sci-Tech Papers[J]. 数据分析与知识发现, 2023, 7(2): 61-71.
[10] Zhang Siyang, Wei Subo, Sun Zhengyan, Zhang Shunxiang, Zhu Guangli, Wu Houyue. Extracting Emotion-Cause Pairs Based on Multi-Label Seq2Seq Model[J]. 数据分析与知识发现, 2023, 7(2): 86-96.
[11] Lyu Xueqiang, Du Yifan, Zhang Le, Pan Huiping, Tian Chi. GKTR Retrieval Model for Engineering Consulting Reports with Graph Convolution Topological and Keyword Features[J]. 数据分析与知识发现, 2023, 7(12): 155-163.
[12] Wu Xuxu, Chen Peng, Jiang Huan. Micro-Blog Fine-Grained Sentiment Analysis Based on Multi-Feature Fusion[J]. 数据分析与知识发现, 2023, 7(12): 102-113.
[13] Gao Haoxin, Sun Lijuan, Wu Jingchen, Gao Yutong, Wu Xu. Online Sensitive Text Classification Model Based on Heterogeneous Graph Convolutional Network[J]. 数据分析与知识发现, 2023, 7(11): 26-36.
[14] 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[J]. 数据分析与知识发现, 2023, 7(10): 109-118.
[15] Li Nan, Wang Bo. Recognition and Visual Analysis of Interdisciplinary Semantic Drift[J]. 数据分析与知识发现, 2023, 7(10): 15-24.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn