The word level shallow convolutional neural network (CNN) model has achieved good performance in text classification tasks. However, shallow CNN models cant capture long range dependencies, which affects the model's performance in text classification tasks, but simply deepening the number of layers of the model does not improve the models performance. This paper proposes a new word level text classification model Word CNN Att, which uses CNN to capture local features and position information, and captures long range dependencies with self attention mechanism. The accuracy of Word CNN Att, on the five public datasets of AGNews, DBPedia, Yelp Review Polarity, Yelp Review Full, Yahoo! Answers, is 0.9%, 0.2%, 0.5%, 2.1%, and 2.0% higher than the word level shallow CNN model respectively.
