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.

翻译：文本分类是自然语言处理（NLP）中的一项基础任务。近期多项研究表明深度学习在文本处理领域的成功。卷积神经网络（CNN）作为一种流行的深度学习模型，在文本分类任务中展现出显著成效。本文研究了基于CNN的文本分类新基线模型。这些模型将文档表示为三维张量输入网络，从而实现句子级分析。采用该方法使模型能够利用文本中句子的位置信息，同时通过分析相邻句子提取额外特征。所提模型与多个数据集上的当前最优模型进行了比较。结果表明，所提模型具有更优性能，尤其在处理长文档时表现更为突出。