Short text entity relation joint extraction based on parallel residual expansion convolutional network
Relationship extraction aims to extract semantic relationships between entity pairs from text,but existing relationship extraction methods suffer from the shortcomings of relationship redundancy and overlap,especially for short texts,which may result in insufficient semantic information and loud noise due to insufficient contextual information.Moreover,conventional pipeline based relation extraction models face error propagation issues.A method of short text entity relation joint extraction based on parallel residual expansion convolutional network is proposed.In this method,BERT(bidirectional encoder representations from transformers)is used to generate semantic feature information,and the parallel residual dilated convolutional network is employed to capture semantic information,thereby enhancing the ability to capture context information and alleviate noise.The joint extraction framework can be used to filter out irrelevant relationships by extracting potential relationships,and extract entities to predict triplets,thus solving the problems of relationship redundancy and overlap,and improving computational efficiency.The experimental results demonstrate that,in comparison with existing mainstream models,the F1 values of the proposed model on the three public datasets NYT,WebNLG and DuIE are 90.9%,91.3%and 73.5%,respectively,which are improved compared with the baseline model,which verifies the effectiveness of the model.
万方数据
维普
