基于逆类别注意力机制的电商文本分类

电商数据所属类别对于分析电商数据有重要意义,基于人力的分类无法适应如今海量的电商数据,基于传统算法模型的分类难以提取有价值的人工特征.本文采用BiLSTM模型并且引入注意力机制,将其应用于电商数据分类中.该模型包括Embedding层、BiLSTM层、注意力机制层和输出层.Embedding层加载Word2Vec开源工...

E-Commerce Text Classification Based on Reverse Category Attention Mechanism

The category of e-commerce data is of great significance for its analysis. The classification based on human resources cannot adapt to the massive e-commerce data nowadays, and the classification based on traditional algorithm models can hardly extract valuable artificial features. In this study, the BiLSTM model integrated with an attention mechanism is introduced to classify e-commerce data. The model includes embedding layer, BiLTM layer, attention mechanism layer, and output layer. The embedding layer loads the word vector trained by Word2Vec; the BiLSTM layer captures the context of each word; the attention mechanism layer allocates weights for each word to synthesize new sample features. The experimental results show that the classification accuracy of the attention mechanism based on the inverse class frequency reaches 91.93%, which is improved compared with the BiLSTM model without the attention mechanism and other attention mechanisms introduced. This model has a good effect in the classification of e-commerce data and points out a new thinking direction for the introduction of attention mechanisms.