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

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 109952/140888 (78%)
Visitors : 46264166 Online Users : 837

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

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processing systems;Random processes;Conditional random field;Digital humanities;Document structure;Harvard University;Historical documents;Language model;Pattern based method;Text mining;Big data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Abstract:	Person names and location names are essential building blocks for identifying events and social networks in historical documents that were written in literary Chinese.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based and conditional-random-field based methods, and extend our work to mining the document structures in historical documents.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

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML2	424	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...