Classification of legislations using deep learning

Abstract EN

Laws are often developed in a piecemeal approach and many provisions of similar nature are often found in different legislations.

Therefore, there is a need to classify legislations into various legal topics to help legal professionals in their daily activities.

In this study, we have experimented with various deep learning architectures for the automatic classification of 490 legislations from the Republic of Mauritius into 30 categories.

Our results demonstrate that a Deep Neural Network (DNN) with three hidden layers delivered the best performance compared with other architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

A mean classification accuracy of 60.9% was achieved using DNN, 56.5% for CNN and 33.7% for Long Short-Term Memory (LSTM).

Comparisons were also made with traditional machine learning classifiers such as support vector machines and decision trees and it was found that the performance of DNN was superior, by at least 10%, in all runs.

Both general pre-trained word embeddings such as Word2vec and domain-specific word embeddings such as Law2vec were used in combination with the above deep learning architectures but Word2vec had the best performance.

To our knowledge, this is the first application of deep learning in the categorisation of legislations.