@InProceedings{dasgupta-EtAl:2016:COLINGDEMO,
  author    = {Dasgupta, Tirthankar  and  Dey, Lipika  and  Dey, Prasenjit  and  Saha, Rupsa},
  title     = {A Framework for Mining Enterprise Risk and Risk Factors from News Documents},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {180--184},
  abstract  = {Any real world events or trends that can affect the company's growth trajectory
	can be considered as risk. There has been a growing need to automatically
	identify, extract and analyze risk related statements from news events. In this
	demonstration, we will present a risk analytics framework that processes
	enterprise project management reports in the form of textual data and news
	documents and classify them into valid and invalid risk categories. The
	framework also extracts information from the text pertaining to the different
	categories of risks like their possible cause and impacts. Accordingly, we have
	used machine learning based techniques and studied different linguistic
	features like n-gram, POS, dependency, future timing, uncertainty factors in
	texts and their various combinations. A manual annotation study from management
	experts using risk descriptions collected for a specific organization was
	conducted to evaluate the framework. The evaluation showed promising results
	for automated risk analysis and identification.},
  url       = {http://aclweb.org/anthology/C16-2038}
}