Classification of Legislations using Deep Learning
Sameerchand Pudaruth1,
Sunjiv Soyjaudah2, and Rajendra Gunputh3
1ICT Department,
University of Mauritius, Mauritius
2Soyjaudah Chambers,
Mauritius
3Law Department,
University of Mauritius, Mauritius
Abstract: 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.
Keywords: Deep learning, neural networks, classification,
legislations.
Received October 17, 2019; accepted February
9, 2021