基于最短依存路径和集成学习的化学物蛋白质关系抽取  

Chemical Protein Relation Extraction Based on Shortest Dependency Path and Ensemble Learning

作　　者：程威 邵一帆 钱龙华 周国栋 CHENG Wei;SHAO Yifan;QIAN Longhua;ZHOU Guodong(Natural Language Processing Lab,School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院自然语言处理实验室,江苏苏州215006

出　　处：《中文信息学报》2021年第4期58-65,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61976147,61373096)。

摘　　要：化学物与蛋白质之间的相互作用关系抽取对精准医学和药物发现等方面的研究有着重要作用。该文提出了一种基于最短依存路径和注意力机制的双向LSTM模型,并将其应用于化学物蛋白质关系抽取。在特征上综合考虑了最短依存路径上的词性、位置和依存关系类型等。在BioCreative VI CHEMPROT任务上的实验表明,该方法在基于依存信息的系统中获得了较好的F1值性能。同时,集成学习也进一步提高了化学物蛋白质关系抽取性能。The extraction of interaction between chemical and protein plays an important role in the research of precision medicine and drug discovery.This paper proposes a Bi-LSTM model based on the shortest dependency path and attention mechanism,and applies it to chemical protein relation extraction.In terms of features,part-of-speech,position and dependency type on the shortest dependent path are considered.Experiments on the BioCreative VI CHEMPROT task show that the proposed method achieves better F1-value performance than systems based on dependency information.At the same time,the ensemble method further improves the performance of chemical protein relation extraction.

关 键 词：实体关系抽取 化学物蛋白质关系抽取 最短依存路径 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]