  The tiled convolutional neural network (tiled CNN) has been applied only to
computer vision for learning invariances. We adjust its architecture to NLP to
improve the extraction of the most salient features for sentiment analysis.
Knowing that the major drawback of the tiled CNN in the NLP field is its
inflexible filter structure, we propose a novel architecture called hybrid
tiled CNN that applies a filter only on the words that appear in the similar
contexts and on their neighbor words (a necessary step for preventing the loss
of some n-grams). The experiments on the datasets of IMDB movie reviews and
SemEval 2017 demonstrate the efficiency of the hybrid tiled CNN that performs
better than both CNN and tiled CNN.
