@inproceedings{nghiem-etal-2022-text,
title = "Text Classification and Prediction in the Legal Domain",
author = "Nghiem, Minh-Quoc and
Baylis, Paul and
Freitas, Andr{\'e} and
Ananiadou, Sophia",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.504",
pages = "4717--4722",
abstract = "We present a case study on the application of text classification and legal judgment prediction for flight compensation. We combine transformer-based classification models to classify responses from airlines and incorporate text data with other data types to predict a legal claim being successful. Our experimental evaluations show that our models achieve consistent and significant improvements over baselines and even outperformed human prediction when predicting a claim being successful. These models were integrated into an existing claim management system, providing substantial productivity gains for handling the case lifecycle, currently supporting several thousands of monthly processes.",
}
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<abstract>We present a case study on the application of text classification and legal judgment prediction for flight compensation. We combine transformer-based classification models to classify responses from airlines and incorporate text data with other data types to predict a legal claim being successful. Our experimental evaluations show that our models achieve consistent and significant improvements over baselines and even outperformed human prediction when predicting a claim being successful. These models were integrated into an existing claim management system, providing substantial productivity gains for handling the case lifecycle, currently supporting several thousands of monthly processes.</abstract>
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%0 Conference Proceedings
%T Text Classification and Prediction in the Legal Domain
%A Nghiem, Minh-Quoc
%A Baylis, Paul
%A Freitas, André
%A Ananiadou, Sophia
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F nghiem-etal-2022-text
%X We present a case study on the application of text classification and legal judgment prediction for flight compensation. We combine transformer-based classification models to classify responses from airlines and incorporate text data with other data types to predict a legal claim being successful. Our experimental evaluations show that our models achieve consistent and significant improvements over baselines and even outperformed human prediction when predicting a claim being successful. These models were integrated into an existing claim management system, providing substantial productivity gains for handling the case lifecycle, currently supporting several thousands of monthly processes.
%U https://aclanthology.org/2022.lrec-1.504
%P 4717-4722
Markdown (Informal)
[Text Classification and Prediction in the Legal Domain](https://aclanthology.org/2022.lrec-1.504) (Nghiem et al., LREC 2022)
ACL
- Minh-Quoc Nghiem, Paul Baylis, André Freitas, and Sophia Ananiadou. 2022. Text Classification and Prediction in the Legal Domain. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4717–4722, Marseille, France. European Language Resources Association.