A Framework for Mining Enterprise Risk and Risk Factors from News Documents

Tirthankar Dasgupta, Lipika Dey, Prasenjit Dey, Rupsa Saha


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.
Anthology ID:
C16-2038
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
180–184
Language:
URL:
https://www.aclweb.org/anthology/C16-2038
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-2038.pdf