Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text
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:
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C18-2034.pdf