  Recently, implicit representation models, such as embedding or deep learning,
have been successfully adopted to text classification task due to their
outstanding performance. However, these approaches are limited to small- or
moderate-scale text classification. Explicit representation models are often
used in a large-scale text classification, like the Open Directory Project
(ODP)-based text classification. However, the performance of these models is
limited to the associated knowledge bases. In this paper, we incorporate word
embeddings into the ODP-based large-scale classification. To this end, we first
generate category vectors, which represent the semantics of ODP categories by
jointly modeling word embeddings and the ODP-based text classification. We then
propose a novel semantic similarity measure, which utilizes the category and
word vectors obtained from the joint model and word embeddings, respectively.
The evaluation results clearly show the efficacy of our methodology in
large-scale text classification. The proposed scheme exhibits significant
improvements of 10% and 28% in terms of macro-averaging F1-score and precision
at k, respectively, over state-of-the-art techniques.
