基于多任务学习的生物医学实体关系抽取

生物医学实体关系抽取是生物医学文本挖掘领域的一项重要任务,它可以自动从生物医学文本中挖掘实体间的相互关系。目前,生物医学实体关系抽取方法一般只针对某一特定任务(如药物关系,蛋白质交互关系抽取等)训练单任务模型进行抽取,忽略了多个任务之间的相关性。因此,该文使用基于神经网络的多任务学习方法对多个生物医学关系抽取任务间的关联性进行了探索。首先构建了全共享模型和私有共享模型,然后在此基础上提出了一种基于Attention机制的主辅多任务模型。在生物医学领域关系抽取的5个公开数据集上的实验结果表明,该文的多任务学习方法可以有效地在学习任务之间共享信息,使得任务间互相促进,获得了比单任务方法更好的关系抽取结果。

A Multi-task Learning Approach to Biomedical Entity Relation Extraction

Biomedical relation extraction plays an important role in biomedical text mining, which can automatically extract high-quality biomedical relationships from biomedical texts. In this paper, we apply neural network-based multi-task learning method to explore the correlation among multiple biomedical relation extraction tasks. In our study, we construct a fully-shared model (FSM) and a shared-private model (SPM) and propose an attention-based main-auxiliary model (Att-MAM). Experimental results on five public biomedical relation extraction datasets show that the multi-task learning can obtain better performance than the single task method.