id: 05547714
dt: a
an: 05547714
au: Lu, Hsin-Min; Huang, Nina WanHsin; Zhang, Zhu; Chen, Tsai-Jyh
ti: Identifying firm-specific risk statements in news articles.
so: Chen, Hsinchun (ed.) et al., Intelligence and security informatics. Pacific
    Asia workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009.
    Proceedings. Berlin: Springer (ISBN 978-3-642-01392-8/pbk). Lecture
    Notes in Computer Science 5477, 42-53 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: risk management; epistemic modality; evidentiality; machine learning
ci: 
li: doi:10.1007/978-3-642-01393-5_6
ab: Summary: 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.
rv: