Biomedical document relation extraction with prompt learning and KNN

Abstract

Document-level relation extraction is designed to recognize connections between entities a cross sentences or between sentences. The current mainstream document relation extraction model is mainly based on the graph method or combined with the pre-trained language model, which leads to the relatively complex process of the whole workflow. In this work, we propose biomedical relation extraction based on prompt learning to avoid complex relation extraction processes and obtain decent performance. Particularity, we present a model that combines prompt learning with T5 for document relation extraction, by integrating a mask template mechanism into the model. In addition, this work also proposes a few-shot relation extraction method based on the K-nearest neighbor (KNN) algorithm with prompt learning. We select similar semantic labels through KNN, and subsequently conduct the relation extraction. The results acquired from two biomedical document benchmarks indicate that our model can improve the learning of document semantic information, achieving improvements in the relation F1 score of 3.1% on CDR.