  Text classification is a fundamental task in natural language processing
(NLP). Several recent studies show the success of deep learning on text
processing. Convolutional neural network (CNN), as a popular deep learning
model, has shown remarkable success in the task of text classification. In this
paper, new baseline models have been studied for text classification using CNN.
In these models, documents are fed to the network as a three-dimensional tensor
representation to provide sentence-level analysis. Applying such a method
enables the models to take advantage of the positional information of the
sentences in the text. Besides, analysing adjacent sentences allows extracting
additional features. The proposed models have been compared with the
state-of-the-art models using several datasets. The results have shown that the
proposed models have better performance, particularly in the longer documents.
