Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text

Rahul Nair, Killian Levacher, Martin Stephenson

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Abstract
Large organizations spend considerable resources in reviewing regulations and ensuring that their business processes are compliant with the law. To make compliance workflows more efficient and responsive, we present a system for machine-driven annotations of legal documents. A set of natural language processing pipelines are designed and aimed at addressing some key questions in this domain: (a) is this (new) regulation relevant for me? (b) what set of requirements does this law impose?, and (c) what is the regulatory intent of a law? The system is currently undergoing user trials within our organization.
Anthology ID:
C18-2034
Volume:
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Editor:
Dongyan Zhao
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
157–160
Language:
URL:
https://aclanthology.org/C18-2034
DOI:
Bibkey:
Cite (ACL):
Rahul Nair, Killian Levacher, and Martin Stephenson. 2018. Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 157–160, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text (Nair et al., COLING 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C18-2034.pdf