Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets

Thomas Vacek, Ronald Teo, Dezhao Song, Timothy Nugent, Conner Cowling, Frank Schilder


Abstract
Dockets contain a wealth of information for planning a litigation strategy, but the information is locked up in semi-structured text. Manually deriving the outcomes for each party (e.g., settlement, verdict) would be very labor intensive. Having such information available for every past court case, however, would be very useful for developing a strategy because it potentially reveals tendencies and trends of judges and courts and the opposing counsel. We used Natural Language Processing (NLP) techniques and deep learning methods allowing us to scale the automatic analysis of millions of US federal court dockets. The automatically extracted information is fed into a Litigation Analytics tool that is used by lawyers to plan how they approach concrete litigations.
Anthology ID:
W19-2206
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2019
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–54
Language:
URL:
https://aclanthology.org/W19-2206
DOI:
10.18653/v1/W19-2206
Bibkey:
Cite (ACL):
Thomas Vacek, Ronald Teo, Dezhao Song, Timothy Nugent, Conner Cowling, and Frank Schilder. 2019. Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 45–54, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets (Vacek et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2206.pdf