  This paper conducts a comparative study on the performance of various machine
learning (``ML'') approaches for classifying judgments into legal areas. Using
a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how
state-of-the-art NLP methods compare against traditional statistical models
when applied to a legal corpus that comprised few but lengthy documents. All
approaches tested, including topic model, word embedding, and language
model-based classifiers, performed well with as little as a few hundred
judgments. However, more work needs to be done to optimize state-of-the-art
methods for the legal domain.
