Text classification with sparse composite document vectors

In this work, we present a modified feature formation technique gradedweighted Bag of Word Vectors (gwBoWV) by (Vivek Gupta, 2016) for faster and better composite document feature representation. We propose a very simple feature construction algorithm that potentially overcomes many weaknesses in current distributional vector representations and other composite document representation methods widely used for text representation. Through extensive experiments on multi-class classification on 20newsgroup dataset and multi-label text classification on Reuters-21578, we achieve better performance results and also significant reduction in training and prediction time compared to composite document representation methods gwBoWV and TWE(Liu et al., 2015b).