Long Text Abstract Similarity Model Based on Hierarchical Gated Neural Network
Long text feature extraction is a research hotspot in the field of semantic understanding and key information extraction,and how to extract effective information from long text and calculate the similarity between long texts has always been one of the main research directions of natural language processing.Based on this,this paper proposes a long text abstract similarity model based on hierarchical gated neural network.The model is mainly divided into two parts:a)the ab-stract generation based on BiLSTM,and the multi-head-attention mechanism is added on the basis of the BiLSTM model,so that the model can extract deeper features.b)Text similarity calculations based on the abstract,the traditional similarity classification model is transformed into a regression model,and the multi-layer BiLSTM is used to extract the features of the generated abstract and add adaptive factors as gating to control the output of each layer of BiLSTM information.
long text similarityBiLSTMfeature extractioncosine similaritymulti-head-attention
NETL
NSTL
万方数据
