Identifying Firm-Specific Risk Statements in News Articles

被引：0
|
作者：
Lu, Hsin-Min [1 ]
Huang, Nina WanHsin [1 ]
Zhang, Zhu [1 ]
Chen, Tsai-Jyh [2 ]
机构：
[1] Univ Arizona, Dept Management Informat Syst, 1130 E Helen St, Tucson, AZ 85721 USA
[2] Natl Chengchi Univ, Dept Risk Management &Insurance, No 64 Sec 2 ZhiNan Rd Wenshan District, Taipei 11605, Taiwan
来源：
INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS | 2009年 / 5477卷
基金：
美国国家科学基金会;
关键词：
Risk management; epistemic modality; evidentiality; machine learning;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
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.
引用
收藏
页码：42 / +
页数：3
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