近日，硕士生孙逸的研究成果被IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)录用，TCBB为CCF推荐期刊B类，SCI三区期刊。

  标题：Knowledge Guided Attention and Graph Convolutional Networks for Chemical-Disease Relation Extraction（知识引导注意力和图卷积网络的化学物-疾病关系抽取）

  摘要：The automatic extraction of the chemical-disease relation (CDR) from the text becomes critical because it takes a lot of time and effort to extract valuable CDR manually. Studies have shown that prior knowledge from the biomedical knowledge base is important for relation extraction. The method of combining deep learning models with prior knowledge is worthy of our study. In this paper, we propose a new model called Knowledge Guided Attention and Graph Convolutional Networks (KGAGN) for CDR extraction. First, to make full advantage of domain knowledge, we train entity embedding as a feature representation of input sequence, and relation embedding to capture weighted contextual information further through the attention mechanism. Then, to make full advantage of syntactic dependency information in cross-sentence CDR extraction, we construct document-level syntactic dependency graphs and encode them using a graph convolution network (GCN). Finally, the chemical-induced disease (CID) relation is extracted by using weighted context features and long-range dependency features both of which contain additional knowledge information We evaluated our model on the CDR dataset published by the BioCreative-V community and achieves an F1-score of 73.3%, surpassing other state-of-the-art methods. 

  因为手动提取有价值的化学物-疾病关系（CDR）需要花费大量的时间和精力，从文本中自动提取关系变得至关重要。研究表明，生物医学知识库中的先验知识对于关系抽取非常重要，深度学习模型与先验知识相结合的方法值得我们研究。本文提出了一个新的模型，称为知识引导注意力和图卷积网络（KGAGN）。首先，为了充分利用领域知识，训练实体嵌入作为输入序列的特征表示；关系嵌入通过注意力机制进一步捕获加权上下文信息。然后，为了充分利用跨句CDR提取中的句法依赖信息，构建文档级句法依赖图，并使用图卷积网络（GCN）对其进行编码。最后，通过使用加权上下文特征和远距离依赖特征来提取化学诱导疾病（CID）关系，这两个特征都包含额外的知识信息。