Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference-猫眼课题宝

DOI：

10.18653/v1/2021.acl-long.488

发表时间：

2021-05

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

T. Lai;Heng Ji;ChengXiang Zhai;Quan Hung Tran

与一般新闻领域相比，生物医学文本的信息提取需要更广泛的领域知识。然而，许多以前的IE方法在推理过程中没有利用任何外部知识。由于生物医学出版物呈指数增长，不超出其固定参数集的模型可能会落后。受人类如何查找相关信息以理解科学文本的启发，我们提出了一种利用外部知识进行联合实体和关系提取的新框架，称为KECI（知识增强集体推理）。给定一个输入文本，KECI首先构造一个初始跨度图，表示它对文本的初始理解。然后，它使用实体链接器形成包含文本中提到的实体相关背景知识的知识图。为了做出最终的预测，KECI使用注意机制将初始的跨度图和知识图融合成一个更精细的图。KECI采用一种集体方法，通过使用图卷积网络将全局关系信息集成到局部表示中，将提及范围链接到实体。我们的实验结果表明，该框架是非常有效的，在两个不同的基准数据集：BioRelEx（结合相互作用检测）和ADE（药物不良事件提取）中取得了最新的结果。例如，KECI在BioRelEx实体和关系提取任务上的F1分数比最先进的F1分数分别提高了4.59%和4.91%

Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-the-art results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity and relation extraction tasks

Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference

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