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Semantic textual similarity (STS) in the clinical domain helps improvediagnostic efficiency and produce concise texts for downstream data miningtasks. However, given the high degree of domain knowledge involved in clinictext, it remains challenging for general language models to infer implicitmedical relationships behind clinical sentences and output similaritiescorrectly. In this paper, we present a graph-augmented cyclic learningframework for similarity estimation in the clinical domain. The framework canbe conveniently implemented on a state-of-art backbone language model, andimprove its performance by leveraging domain knowledge through co-training withan auxiliary graph convolution network (GCN) based network. We report thesuccess of introducing domain knowledge in GCN and the co-training framework byimproving the Bio-clinical BERT baseline by 16.3% and 27.9%, respectively.

Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes