  Considering that words with different characteristic in the text have
different importance for classification, grouping them together separately can
strengthen the semantic expression of each part. Thus we propose a new text
representation scheme by clustering words according to their latent semantics
and composing them together to get a set of cluster vectors, which are then
concatenated as the final text representation. Evaluation on five
classification benchmarks proves the effectiveness of our method. We further
conduct visualization analysis showing statistical clustering results and
verifying the validity of our motivation.
