Abstract：
BACKGROUND: Biomedical relation extraction at the document level (Bio-DocRE) involves extracting relation instances from biomedical texts that span multiple sentences, often containing various entity concepts such as genes, diseases, chemicals, variants, etc. Currently, this task is usually implemented based on graphs or transformers. However, most work directly models entity features to relation prediction, ignoring the effectiveness of entity pair information as an intermediate state for relation prediction. In this article, we decouple this task into a three-stage process to capture sufficient information for improving relation prediction.
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.
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.
摘要：
背景：文档级别的生物医学关系提取（Bio-DocRE）涉及从跨越多个句子的生物医学文本中提取关系实例，通常包含各种实体概念，如基因，疾病,化学品,变体,等目前,此任务通常基于图形或变压器来实现。然而,大多数工作直接将实体特征建模为关系预测，忽略实体对信息作为关系预测中间状态的有效性。在这篇文章中,我们将此任务分离为一个三阶段过程，以捕获足够的信息来改善关系预测。
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。
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。
