政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111687

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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/111687

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111687

Title:	Mining local gazetteers of literary Chinese with CRF and pattern based methods for biographical information in Chinese history
Authors:	劉昭麟 Liu, Chao-Lin Huang, Chih-Kai Wang, Hongsu Bol, Peter K.
Contributors:	資訊科學系
Keywords:	Computational linguistics;Data mining;History;Natural language processing systems;Random processes;Conditional random field;Digital humanities;Document structure;Harvard University;Historical documents;Language model;Pattern based method;Text mining;Big data
Date:	2015-12
Issue Date:	2017-08-09 17:27:07 (UTC+8)
Abstract:	Person names and location names are essential building blocks for identifying events and social networks in historical documents that were written in literary Chinese. We take the lead to explore the research on algorithmically recognizing named entities in literary Chinese for historical studies with language-model based and conditional-random-field based methods, and extend our work to mining the document structures in historical documents. Practical evaluations were conducted with texts that were extracted from more than 220 volumes of local gazetteers (Difangzhi,). Difangzhi is a huge and the single most important collection that contains information about officers who served in local government in Chinese history. Our methods performed very well on these realistic tests. Thousands of names and addresses were identified from the texts. A good portion of the extracted names match the biographical information currently recorded in the China Biographical Database (CBDB) of Harvard University, and many others can be verified by historians and will become as new additions to CBDB.1 © 2015 IEEE.
Relation:	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, 1629-1638
Data Type:	conference
DOI 連結:	http://dx.doi.org/10.1109/BigData.2015.7363931
DOI:	10.1109/BigData.2015.7363931
Appears in Collections:	[資訊科學系] 會議論文

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