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论文类型：期刊论文

发表时间：2019-11-01

发表刊物：JOURNAL OF BIOMEDICAL INFORMATICS

收录刊物：EI、PubMed、SCIE

卷号：99

页面范围：103294

ISSN号：1532-0464

关键字：Biomedical relation classification; Neural networks; Biomedical literature; Natural language processing; Deep learning

摘要：The explosive growth of biomedical literature has created a rich source of knowledge, such as that on proteinprotein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to automatically detect and classify biomedical relations, which has great benefits for various biomedical research and applications. In the past decade, significant progress has been made in biomedical relation classification. With the advance of neural network methodology, neural network-based approaches have been applied in biomedical relation classification and achieved state-of-the-art performance for some public datasets and shared tasks. In this review, we describe the recent advancement of neural network-based approaches for classifying biomedical relations. We summarize the available corpora and introduce evaluation metrics. We present the general framework for neural network-based approaches in biomedical relation extraction and pretrained word embedding resources. We discuss neural network-based approaches, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We conclude by describing the remaining challenges and outlining future directions.