| dc.creator (作者) | 陳彩稚 | zh_TW |
| dc.creator (作者) | Lu, Hsin-Min ; Huang, Nina WanHsin ; Zhang, Zhu ; Chen , Tsai-Jyh | - |
| dc.date (日期) | 2009-04 | en_US |
| dc.date.accessioned | 6-Oct-2010 10:40:31 (UTC+8) | - |
| dc.date.available | 6-Oct-2010 10:40:31 (UTC+8) | - |
| dc.date.issued (上傳時間) | 6-Oct-2010 10:40:31 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/45767 | - |
| dc.description.abstract (摘要) | Textual data are an important information source for risk management for business organizations. To effectively identify, extract, and analyze risk-related statements in textual data, these processes need to be automated. We developed an annotation framework for firm-specific risk statements guided by previous economic, managerial, linguistic, and natural language processing research. A manual annotation study using news articles from the Wall Street Journal was conducted to verify the framework. We designed and constructed an automated risk identification system based on the annotation framework. The evaluation using manually annotated risk statements in news articles showed promising results for automated risk identification. | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Intelligence and Security Informatics, Springer-Verlag, pp.42-53 | en_US |
| dc.title (題名) | Identifying Firm-Specific Risk Statements in News Articles | en_US |
| dc.type (資料類型) | book/chapter | en |
| dc.identifier.doi (DOI) | 10.1007/978-3-642-01393-5_6 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/978-3-642-01393-5_6 | en_US |
