融合依存信息Attention机制的药物关系抽取研究

药物关系(Drug-Drug Interaction, DDI)抽取是生物医学关系抽取领域的重要分支,现有方法主要强调实体、位置等信息对关系抽取的影响。相关研究表明,依存信息对于关系抽取具有重要作用,如何合理利用依存信息是关系抽取研究中需要解决的问题。该文提出一种融合依存信息 Attention机制的药物关系抽取模型,衡量最短依存路径与句子的相关性,捕捉对实体间关系有用的信息。首先使用双向GRU(BiGRU)网络分别学习原句子和最短依存路径(Shortest Dependency Path,SDP)的语义信息和上下文信息,然后通过Attention机制将SDP信息与原句子信息融合,最后利用融合依存信息之后的句子表示进行分类预测。在DDIExtraction2013语料上进行了实验评估,模型F值为73.72%。

Drug-Drug Interaction Extraction with the Attention Mechanism Over the Dependency

Drug-Drug Interaction (DDI) extraction is an important issue in biomedical relationship extraction. Most of existing methods emphasize the key information such as entities and positions in the sentences. To further exploit the sentence structure, this paper proposes a Drug-Drug interaction extraction model based on the attention mechanism over the dependency. The correlation between the shortest dependency path and the sentence is measured to capture the useful information. Firstly, this model uses BiGRU network to learn the semantic information and context information of the original sentence and the Shortest Dependency Path (SDP) respectively. Secondly, the SDP information is incorporated into the original sentence information through the Attention mechanism. Finally, the final sentence representation is used to classify and predict DDI. This approach is evaluated on DDIExtraction 2013 corpus, yielding a micro F-scores of 73.72%.