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        <title>A Framework for Mining Enterprise Risk and Risk Factors from News Documents</title>
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        <namePart type="given">Lipika</namePart>
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        <namePart type="given">Prasenjit</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Hideo</namePart>
            <namePart type="family">Watanabe</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Framework for Mining Enterprise Risk and Risk Factors from News Documents
%A Dasgupta, Tirthankar
%A Dey, Lipika
%A Dey, Prasenjit
%A Saha, Rupsa
%Y Watanabe, Hideo
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F dasgupta-etal-2016-framework
%X 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.
%U https://aclanthology.org/C16-2038
%P 180-184

Markdown (Informal)

[A Framework for Mining Enterprise Risk and Risk Factors from News Documents](https://aclanthology.org/C16-2038) (Dasgupta et al., COLING 2016)

• A Framework for Mining Enterprise Risk and Risk Factors from News Documents (Dasgupta et al., COLING 2016)

ACL

• Tirthankar Dasgupta, Lipika Dey, Prasenjit Dey, and Rupsa Saha. 2016. A Framework for Mining Enterprise Risk and Risk Factors from News Documents. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 180–184, Osaka, Japan. The COLING 2016 Organizing Committee.