  We investigate the integration of word embeddings as classification features
in the setting of large scale text classification. Such representations have
been used in a plethora of tasks, however their application in classification
scenarios with thousands of classes has not been extensively researched,
partially due to hardware limitations. In this work, we examine efficient
composition functions to obtain document-level from word-level embeddings and
we subsequently investigate their combination with the traditional
one-hot-encoding representations. By presenting empirical evidence on large,
multi-class, multi-label classification problems, we demonstrate the efficiency
and the performance benefits of this combination.
