Document classification using a Bi-LSTM to unclog Brazil's supreme court
Information Retrieval
2018-11-29 v1 Machine Learning
Machine Learning
Abstract
The Brazilian court system is currently the most clogged up judiciary system in the world. Thousands of lawsuit cases reach the supreme court every day. These cases need to be analyzed in order to be associated to relevant tags and allocated to the right team. Most of the cases reach the court as raster scanned documents with widely variable levels of quality. One of the first steps for the analysis is to classify these documents. In this paper we present a Bidirectional Long Short-Term Memory network (Bi-LSTM) to classify these pieces of legal document.
Cite
@article{arxiv.1811.11569,
title = {Document classification using a Bi-LSTM to unclog Brazil's supreme court},
author = {Fabricio Ataides Braz and Nilton Correia da Silva and Teofilo Emidio de Campos and Felipe Borges S. Chaves and Marcelo H. S. Ferreira and Pedro Henrique Inazawa and Victor H. D. Coelho and Bernardo Pablo Sukiennik and Ana Paula Goncalves Soares de Almeida and Flavio Barros Vidal and Davi Alves Bezerra and Davi B. Gusmao and Gabriel G. Ziegler and Ricardo V. C. Fernandes and Roberta Zumblick and Fabiano Hartmann Peixoto},
journal= {arXiv preprint arXiv:1811.11569},
year = {2018}
}
Comments
This work was presented at NIPS 2018 Workshop on Machine Learning for the Developing World (ML4D)