| .As a key technology of information processing,text classification technology has always been a hot issue in academic research.In recent years,deep learning has achieved remarkable results in many fields,and it has also proven to have advanced text representation capabilities in natural language processing tasks.The classical convolutional neural network text classification model ignores the order information of the text.This paper proposes a novel text classification model combining attention mechanism,which can make up for the shortcomings of the traditional classification model by adding the ability of to extract the sequential features and introducing the attention mechanism to optimize the text features.The main research work oh this paper can be summarized as:(1)Traditional cnn text classifition models just pay atttention to n-gram features and ignore sequential features.This paper designs and implements a novel text classification model based on attention machanism.The model has changed convolutional module and combined it with RNN,finally,attention mechanism was introduced to improve the performance of text classification.(2)For the problem that the training speed of ACRNN model is too slow,this paper proposes an improved algorithm for the calculation of attention mechanism.Given an initial value based on TF-IDF for the attention matrix,the attention matrix can be quickly converged,thereby reducing the time of network training.(3)A set of comparative experiments are set up on different text categorization corpus.By comparing the proposed model with several benchmark models,the validity of the model in text classification is proved. |
