Research On Text Matching Algorithm Based On GNN

Text matching is one of the basic tasks in the field of natural language processing(NLP).Aiming at this practical problem and combining with the specific task scene of text matching,this paper designs a text matching algorithm based on the combination of the BERT(Bidirectional Encoder Representation from Transformers)pre-trained model and the graph neural network model.The algorithm can simplify the complex process of text interaction and achieve a better result of text matching.This paper designs a text matching algorithm based on the semantic model and the semantic model of the paper.The part of text semantic matching is based on the pre training model of Bert,adding a variety of attention interaction methods to obtain more level of semantic information.At the same time,combining with the characteristics of specific application scenarios of text matching,the core attention mechanism is modified,so that the attention mechanism can mine the semantic information of text matching at a deeper level;then,local vector information enhancement is introduced Through average pooling and maximum pooling operations,the obtained enhanced vectors reduce over fitting,and retain the main features of sentences to improve the generalization ability of the model;after pooling operation,through full connection network dimensionality reduction,the final text vector of semantic matching part is obtained.In the syntactic dependency matching part,the syntactic dependency structure is transformed into a graph structure by using the syntactic dependency structure of the sentence;then the graph structure is encoded by using the graph neural network,which is to extract the syntactic structure information of the sentence itself to add the syntactic features of the text matching;and then the average pooling and maximum pooling are used to obtain the whole page The feature vectors of the graph are weighted and integrated through the full connection layer.Finally,the vectors obtained from the semantic matching part and syntactic dependency matching part are spliced,and the final vectors are used to complete the specific task of text matching.In the experimental part of the paper,the Pytorch deep neural network framework is used to develop the algorithm model.After the development is completed,experiments are performed on multiple public data sets in the text matching field,and the results verify the effectiveness of the model.