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/fix-dup-bibkey/C18-2034.pdf