Scalable Methods for Annotating Legal-Decision Corpora
Lisa Ferro, John Aberdeen, Karl Branting, Craig Pfeifer, Alexander Yeh, Amartya Chakraborty
Abstract
Recent research has demonstrated that judicial and administrative decisions can be predicted by machine-learning models trained on prior decisions. However, to have any practical application, these predictions must be explainable, which in turn requires modeling a rich set of features. Such approaches face a roadblock if the knowledge engineering required to create these features is not scalable. We present an approach to developing a feature-rich corpus of administrative rulings about domain name disputes, an approach which leverages a small amount of manual annotation and prototypical patterns present in the case documents to automatically extend feature labels to the entire corpus. To demonstrate the feasibility of this approach, we report results from systems trained on this dataset.- Anthology ID:
- W19-2202
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2019
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Nikolaos Aletras, Elliott Ash, Leslie Barrett, Daniel Chen, Adam Meyers, Daniel Preotiuc-Pietro, David Rosenberg, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–20
- Language:
- URL:
- https://aclanthology.org/W19-2202
- DOI:
- 10.18653/v1/W19-2202
- Cite (ACL):
- Lisa Ferro, John Aberdeen, Karl Branting, Craig Pfeifer, Alexander Yeh, and Amartya Chakraborty. 2019. Scalable Methods for Annotating Legal-Decision Corpora. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 12–20, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Scalable Methods for Annotating Legal-Decision Corpora (Ferro et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-2202.pdf