一种基于嵌入式注意力机制的文本分类方法

Text Classification Method Based on Embeddable Attention Mechanism

大数据时代海量的文本数据蕴含着极大的科研价值,文本分类技术得到广泛的关注。文本分类在信息检索、自动问答等应用领域占据重要地位,是自然语言处理研究的关键技术之一。本文针对神经网络分类方法训练时间长性能仍可提高,提出一种嵌入式注意力机制模块(Eam),用来增强已有的文本分类神经网络模型。该模块将重点关注数据中什么是最有意义及哪里含有的信息量更为丰富,从而高效提取文本中有价值的信息区域加速模型收敛。本文以增强TextCNN、ImdbCNN为例,在公开数据集IMDB上证明Eam的有效性,同等参数配置情况下能够提升模型的准确率、召回率及F1值,较原模型能够更快收敛减少训练时间。

The huge amount of text data in the era of big data contains great scientific research value,and the text classification technology has received widespread attention.Text classification occupies an important position in the fields of information retrieval,automatic question answering and other applications,and is one of the key technologies in natural language processing research.Aiming at the long training time performance of neural network classification method,this paper proposes an embedded attention mechanism module(Eam)to enhance the existing neural network model of text classification.This module will focus on what is most meaningful in the data and where the amount of information is more abundant,so as to efficiently extract valuable information areas in the text and accelerate model convergence.This paper takes Enhanced TextCNN and ImdbCNN as examples,and proves the validity of Eam on the public dataset IMDB.It can improve the accuracy,recall and F1 value of the model under the same parameter configuration,which can converge faster and reduce the training time than the original model.