Text Classification Method Based on Word2Vec and AlexNet-2 with Improved AttentionMechanism

Abstract

In order to improve the accuracy and efficiency of text classification, a text classification method based on Word2Vec text representation and AlexNet-2 with improved attention mechanism is proposed.Firstly, Word2Vec is adopted to embed the text word features, and the word vector is trained to represent the text in the form of distributed vectors.Then, an improved AlexNet-2 is used to effectively encode the long-distance word dependency.Meanwhile, the attention mechanism is added to the model to learn the contextual embedding semantics of the target word efficiently, and the word weight is adjusted according to the correlation between the input of word vector and the final prediction result.The experiment is evaluated in three public data sets, and the situations of a large number of sample annotations and a small number of sample annotations are analyzed.Experimental results show that, compared with the existing excellent methods, the proposed method can significantly improve the performance and efficiency of text classification.

Keywords

• text classification|attention mechanism|alexnet-2 model|contextual embedding|word dependency