To address the issue of inadequate information extraction from documents during the training process of traditional document relevance identification methods,a deep learning method based on maximizing mutual information was proposed.An unsupervised learning process that utilized global and local mutual information to learn document representations was involved,with the aim of maximizing the mutual information between the input and output representations of the neural network.The extraction of more comprehensive document content and structural information was enabled,resulting in improved model predic-tion results.Evaluation on multiple tasks demonstrates the feasibility and effectiveness of the proposed method which performs comparably or even better in accuracy than that of some traditional methods.
维普
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