Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service

TL;DR

This paper explores NLP techniques, especially Word2Vec and RNNs, to automate legal document classification, significantly improving efficiency in categorizing petitions by law area with high accuracy.

Contribution

It introduces a domain-specific Word2Vec embedding combined with RNNs for legal document classification, achieving high accuracy and reducing manual effort.

Findings

Best model achieved 90% accuracy

Word2Vec with RNN outperformed other methods

Effective for classifying 18 law categories

Abstract

In recent years, there has been an increased interest in the application of Natural Language Processing (NLP) to legal documents. The use of convolutional and recurrent neural networks along with word embedding techniques have presented promising results when applied to textual classification problems, such as sentiment analysis and topic segmentation of documents. This paper proposes the use of NLP techniques for textual classification, with the purpose of categorizing the descriptions of the services provided by the Public Prosecutor's Office of the State of Paran\'a to the population in one of the areas of law covered by the institution. Our main goal is to automate the process of assigning petitions to their respective areas of law, with a consequent reduction in costs and time associated with such process while allowing the allocation of human resources to more complex tasks. In this paper, we compare different approaches to word representations in the aforementioned task: including document-term matrices and a few different word embeddings. With regards to the classification models, we evaluated three different families: linear models, boosted trees and neural networks. The best results were obtained with a combination of Word2Vec trained on a domain-specific corpus and a Recurrent Neural Network (RNN) architecture (more specifically, LSTM), leading to an accuracy of 90\% and F1-Score of 85\% in the classification of eighteen categories (law areas).