RESULTS: We propose an innovative framework HTGRS for Bio-DocRE, which constructs a hierarchical tree graph (HTG) to integrate key information sources in the document, achieving relation reasoning based on entity. In addition, inspired by the idea of semantic segmentation, we conceptualize the task as a table-filling problem and develop a relation segmentation (RS) module to enhance relation reasoning based on the entity pair. Extensive experiments on three datasets show that the proposed framework outperforms the state-of-the-art methods and achieves superior performance.
METHODS: Our source code is available at https://github.com/passengeryjy/HTGRS.
结果:我们为Bio-DocRE提出了一个创新的框架HTGRS,它构造了一个层次树图(HTG)来集成文档中的关键信息源,实现基于实体的关系推理。此外,受到语义分割思想的启发,我们将任务概念化为表填充问题,并开发了关系分割(RS)模块来增强基于实体对的关系推理。在三个数据集上的大量实验表明,所提出的框架优于最先进的方法,并实现了卓越的性能。
方法:我们的源代码可在https://github.com/pusheryjy/HTGRS获得。
背景:补充数据可在Bioinformatics在线获得。