@inproceedings{vacek-etal-2019-litigation-analytics,
title = "Litigation Analytics: Case Outcomes Extracted from {US} Federal Court Dockets",
author = "Vacek, Thomas and
Teo, Ronald and
Song, Dezhao and
Nugent, Timothy and
Cowling, Conner and
Schilder, Frank",
editor = "Aletras, Nikolaos and
Ash, Elliott and
Barrett, Leslie and
Chen, Daniel and
Meyers, Adam and
Preotiuc-Pietro, Daniel and
Rosenberg, David and
Stent, Amanda",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2206",
doi = "10.18653/v1/W19-2206",
pages = "45--54",
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.",
}
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<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.</abstract>
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<start>45</start>
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%0 Conference Proceedings
%T Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets
%A Vacek, Thomas
%A Teo, Ronald
%A Song, Dezhao
%A Nugent, Timothy
%A Cowling, Conner
%A Schilder, Frank
%Y Aletras, Nikolaos
%Y Ash, Elliott
%Y Barrett, Leslie
%Y Chen, Daniel
%Y Meyers, Adam
%Y Preotiuc-Pietro, Daniel
%Y Rosenberg, David
%Y Stent, Amanda
%S Proceedings of the Natural Legal Language Processing Workshop 2019
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F vacek-etal-2019-litigation-analytics
%X 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.
%R 10.18653/v1/W19-2206
%U https://aclanthology.org/W19-2206
%U https://doi.org/10.18653/v1/W19-2206
%P 45-54
Markdown (Informal)
[Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets](https://aclanthology.org/W19-2206) (Vacek et al., NAACL 2019)
ACL