Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China.
In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documen...
| Publicado en: | Information Technology & Libraries Vol. 40; no. 3; pp. 1 - 24 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
American Library Association
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152572644&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152572644 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07309295 ITL jtl: Information Technology & Libraries issn: 07309295 maglogo: N pubinfo: dt: 2021 vid: 40 iid: 3 pid: 55 pub: American Library Association place: Chicago, Illinois artinfo: ui: 152572644 152572644 152572644 10.6017/ital.v40i3.13279 152572644 ppf: 1 ppct: 23 formats: fmt: @attributes: type: P tig: atl: Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China. aug: au: Zhiyu Wang Jingyu Wu Guang Yu Zhiping Song affil: PhD Candidate, School of Management, Harbin Institute of Technology sug: subj: Archives History Machine Learning Utilization Documentation History Data Analysis Human China Support Vector Machine Algorithms Catalogs Data Mining Databases Correlation Coefficient Materials Management Pearson's Correlation Coefficient Artificial Intelligence Cataloging Databases Data Analysis Software Funding Source ab: In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective. This technology solves the problem of large-scale historical data analysis that is difficult for humans to read and intuitively understand. In this study, we use the historical documents of the Qing Dynasty Hetu Dangse, preserved in the Archives of Liaoning Province, as data analysis samples. China's Hetu Dangse is the largest Qing Dynasty thematic archive with Manchu and Chinese characters in the world. Through word frequency analysis, correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of historical archives, improve the efficient use of historical materials as well as build connections between historical knowledge. Through this, archivists can be guided practically in historical materials' management and compilation. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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