School of Computer Science and Engineering, Dalian Minzu University, 116650 Dalian, China.
School of Computer Science and Technology, Dalian University of Technology, 116024 Dalian, China.
J Biomed Inform. 2023 Sep;145:104459. doi: 10.1016/j.jbi.2023.104459. Epub 2023 Jul 31.
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.
文档级关系抽取旨在识别句子之间或句子之间的实体之间的联系。目前主流的文档关系抽取模型主要基于图方法或结合预训练语言模型，这导致整个工作流程相对复杂。在这项工作中，我们提出了基于提示学习的生物医学关系抽取方法，以避免复杂的关系抽取过程并获得良好的性能。具体来说，我们提出了一种将提示学习与 T5 相结合的模型用于文档关系抽取，通过在模型中集成屏蔽模板机制。此外，这项工作还提出了一种基于提示学习的 K-最近邻（KNN）算法的少样本关系抽取方法。我们通过 KNN 选择相似的语义标签，然后进行关系抽取。在两个生物医学文档基准上的结果表明，我们的模型可以改善文档语义信息的学习，在 CDR 上的关系 F1 得分提高了 3.1%。
