  Information planning enables faster learning with fewer training examples. It
is particularly applicable when training examples are costly to obtain. This
work examines the advantages of information planning for text data by focusing
on three supervised models: Naive Bayes, supervised LDA and deep neural
networks. We show that planning based on entropy and mutual information
outperforms random selection baseline and therefore accelerates learning.
