| dc.contributor | 資訊科學系 | zh_Tw |
| dc.creator (作者) | 劉昭麟 | zh_TW |
| dc.creator (作者) | Liu, Chao-Lin | en_US |
| dc.creator (作者) | Huang, Chih-Kai | en_US |
| dc.creator (作者) | Wang, Hongsu | en_US |
| dc.creator (作者) | Bol, Peter K. | en_US |
| dc.date (日期) | 2015-12 | en_US |
| dc.date.accessioned | 9-Aug-2017 17:27:07 (UTC+8) | - |
| dc.date.available | 9-Aug-2017 17:27:07 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-Aug-2017 17:27:07 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111687 | - |
| dc.description.abstract (摘要) | Person names and location names are essential building blocks for identifying events and social networks in historical documents that were written in literary Chinese.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based and conditional-random-field based methods, and extend our work to mining the document structures in historical documents.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. | en_US |
| dc.format.extent | 212 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, 1629-1638 | en_US |
| dc.subject (關鍵詞) | 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systems; Random processes; Conditional random field; Digital humanities; Document structure; Harvard University; Historical documents; Language model; Pattern based method; Text mining; Big data | en_US |
| dc.title (題名) | Mining local gazetteers of literary Chinese with CRF and pattern based methods for biographical information in Chinese history | en_US |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/BigData.2015.7363931 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1109/BigData.2015.7363931 | |
