An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

The classification of legal documents has been receiving considerate attention over the last few years. This is mainly because of the over-increasing amount of legal information that is being produced on a daily basis in the courts of law. In the Republic of Mauritius alone, a total of 141,164 cases were lodged in the different courts in the year 2015. The Judiciary of Mauritius is becoming more efficient due to a number of measures which were implemented and the number of cases disposed of in each year has also risen significantly; however, this is still not enough to catch up with the increase in the number of new cases that are lodged. In this paper, we used the k-nearest neighbour machine learning classifier in a novel way. Unlike news article, judgments are complex documents which usually span several pages and contains a variety of information about a case. Our approach consists of splitting the documents into equal-sized segments. Each segment is then classified independently of the others. The selection of the predicted category is then done through a plurality voting procedure. Using this novel approach, we have been able to classify law cases with an accuracy of over 83.5%, which is 10.5% higher than when using the whole documents dataset. To the best of our knowledge, this type of process has never been used earlier to categorise legal judgments or other types of documents. In this work, we also propose a new measure called confusability to measure the degree of scatteredness in a confusion matrix.