COVID‐19 clinical medical relationship extraction based on MPNet

Abstract

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID‐19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID‐19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional‐GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre‐trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID‐19 clinical text entity relation extraction task.

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“…MPNet [43][44][45], or Masked and Permuted Pre-training for Language Understanding, is an innovative model in the field of Natural Language Processing (NLP) that extends the capabilities of traditional transformer-based models. Developed by Microsoft, MPNet introduces a novel pre-training method that enhances the model's understanding of language context and structure.…”