Mmf-re: psychological medicine entity relation extraction model based on multi-level feature enhancement

The application of deep learning technology in medical text parsing has continuously progressed. Achieving high-quality extraction of complex entity relationships in medical texts has become an important research direction. Aiming at the problems of lengthy paragraphs and complex sentences in the field of psychological medicine, and the limited feature extraction ability of existing neural network relation extraction models, a psychological medicine entity relation extraction model MMF-RE, with multi-level and multi-unit gated convolution feature enhancement is proposed. The proposed MFE-BERT combines and outputs all the internal encoder layer features based on the pre-trained model to improve the semantic representation ability of the feature vector. At the same time, a multi-unit gated convolutional network is constructed, which can effectively extract multi-granularity local features to perceive long entities. Finally, the FNNAttention mechanism is applied to the model to strengthen the word-level relationship through the forward neural network function. The experimental results show that in the self-built psychological medicine dataset and the biomedical public dataset, the F1 value of the psychological medicine entity relation extraction of the MMF-RE composite neural network model reaches 88.49% and 87.08%, which is better than the existing evaluation indicators, verifying the rationality and effectiveness of the method. This study can provide better help for psychological medicine data analysis.