Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024, China.
BMC Bioinformatics. 2020 Sep 17;21(Suppl 13):380. doi: 10.1186/s12859-020-03673-5.
Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences.
In this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora.
Experimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.
生物医学文献分类是生物医学信息提取的基础，对精准医学至关重要。最近，一些基于神经网络的方法被提出来自动对生物医学文档进行分类。在生物医学领域，文档通常非常长，并且经常包含非常复杂的句子。然而，目前的方法仍然难以跨句子捕捉重要特征。
本文提出了一种用于生物医学文献分类的分层注意力胶囊模型。所提出的模型有效地利用了分层注意力机制和胶囊网络来捕获跨句子的有价值特征，并为文档构建最终的潜在特征表示。我们在三个公共语料库上评估了我们的模型。
实验结果表明，分层注意力机制和胶囊网络在生物医学文献分类任务中都很有帮助。与其他最先进的方法相比，我们的方法表现出很强的竞争力或优越性。
