  Text representation is a fundamental concern in Natural Language Processing,
especially in text classification. Recently, many neural network approaches
with delicate representation model (e.g. FASTTEXT, CNN, RNN and many hybrid
models with attention mechanisms) claimed that they achieved state-of-art in
specific text classification datasets. However, it lacks an unified benchmark
to compare these models and reveals the advantage of each sub-components for
various settings. We re-implement more than 20 popular text representation
models for classification in more than 10 datasets. In this paper, we
reconsider the text classification task in the perspective of neural network
and get serval effects with analysis of the above results.
