Legislative Text Analysis from Judicial Case Reports Using Machine Learning

This paper aims to automate the process of legislative text analysis from judicial case reports to identify applicable law sections for the cases. The proposed methodology combines supervised Machine Learning and Natural Language Processing and is particularly useful for legislative text analysis. The idea is demonstrated with case studies of the Indian Income Tax Act of 1961. We introduced a dataset of case descriptions with applicable law sections from the case details available at the ‘Legal Crystal’ repository. Since multiple law sections could be applied to one particular case, which makes it a multi-label classification problem, we employ two problem transformer techniques, namely one versus rest wrapper and classifier chain, over the Machine Learning models like Logistic Regression, Naive Bayes, Decision Tree, and Support Vector Machine. Different word embedding techniques like n-gram and tf-idf are combined with classifier models to train and test the dataset. We propose a methodology that includes a data pre-processing block, a pipeline consisting of word embedding, a multi-label wrapper, Machine Learning Models, and an evaluation block. Different combination setups of the pipeline are tested for comparative analysis. A customized deep learning model with word2vec and BERT embedding is also tested for the task. It has been observed that the Support Vector Machine with tf-idf word embedding and classifier chain wrapping outperformed the other conventional models and performed at par with the BERT and deep learning model. Although this work is limited to cases related to income tax laws, the proposed methodology is generic to cover any other legal sections.