融合依存信息Attention机制的药物关系抽取研究

被引：1

作者：

李丽双

钱爽

周安桥

刘阳

郭元凯

机构：

[1] 大连理工大学计算机科学与技术学院

来源：

中文信息学报 | 2019年 / 33卷 / 02期

关键词：

生物医学关系抽取; 药物关系抽取; 依存信息; Attention;

D O I：

暂无

中图分类号：

TP391.1 [文字信息处理]; R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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%。

引用

页码：89 / 96

页数：8

共 20 条

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