  The continually increasing number of documents produced each year
necessitates ever improving information processing methods for searching,
retrieving, and organizing text. Central to these information processing
methods is document classification, which has become an important application
for supervised learning. Recently the performance of these traditional
classifiers has degraded as the number of documents has increased. This is
because along with this growth in the number of documents has come an increase
in the number of categories. This paper approaches this problem differently
from current document classification methods that view the problem as
multi-class classification. Instead we perform hierarchical classification
using an approach we call Hierarchical Deep Learning for Text classification
(HDLTex). HDLTex employs stacks of deep learning architectures to provide
specialized understanding at each level of the document hierarchy.
