@inproceedings{vacek-etal-2019-litigation,
title = "Litigation Analytics: Extracting and querying motions and orders from {US} federal courts",
author = "Vacek, Thomas and
Song, Dezhao and
Molina-Salgado, Hugo and
Teo, Ronald and
Cowling, Conner and
Schilder, Frank",
editor = "Ammar, Waleed and
Louis, Annie and
Mostafazadeh, Nasrin",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-4020",
doi = "10.18653/v1/N19-4020",
pages = "116--121",
abstract = "Legal litigation planning can benefit from statistics collected from past decisions made by judges. Information on the typical duration for a submitted motion, for example, can give valuable clues for developing a successful strategy. Such information is encoded in semi-structured documents called dockets. In order to extract and aggregate this information, we deployed various information extraction and machine learning techniques. The aggregated data can be queried in real time within the Westlaw Edge search engine. In addition to a keyword search for judges, lawyers, law firms, parties and courts, we also implemented a question answering interface that offers targeted questions in order to get to the respective answers quicker.",
}
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<abstract>Legal litigation planning can benefit from statistics collected from past decisions made by judges. Information on the typical duration for a submitted motion, for example, can give valuable clues for developing a successful strategy. Such information is encoded in semi-structured documents called dockets. In order to extract and aggregate this information, we deployed various information extraction and machine learning techniques. The aggregated data can be queried in real time within the Westlaw Edge search engine. In addition to a keyword search for judges, lawyers, law firms, parties and courts, we also implemented a question answering interface that offers targeted questions in order to get to the respective answers quicker.</abstract>
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%0 Conference Proceedings
%T Litigation Analytics: Extracting and querying motions and orders from US federal courts
%A Vacek, Thomas
%A Song, Dezhao
%A Molina-Salgado, Hugo
%A Teo, Ronald
%A Cowling, Conner
%A Schilder, Frank
%Y Ammar, Waleed
%Y Louis, Annie
%Y Mostafazadeh, Nasrin
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F vacek-etal-2019-litigation
%X Legal litigation planning can benefit from statistics collected from past decisions made by judges. Information on the typical duration for a submitted motion, for example, can give valuable clues for developing a successful strategy. Such information is encoded in semi-structured documents called dockets. In order to extract and aggregate this information, we deployed various information extraction and machine learning techniques. The aggregated data can be queried in real time within the Westlaw Edge search engine. In addition to a keyword search for judges, lawyers, law firms, parties and courts, we also implemented a question answering interface that offers targeted questions in order to get to the respective answers quicker.
%R 10.18653/v1/N19-4020
%U https://aclanthology.org/N19-4020
%U https://doi.org/10.18653/v1/N19-4020
%P 116-121
Markdown (Informal)
[Litigation Analytics: Extracting and querying motions and orders from US federal courts](https://aclanthology.org/N19-4020) (Vacek et al., NAACL 2019)
ACL