关系抽取
计算机科学
萃取(化学)
互联网
人工智能
集合(抽象数据类型)
数据提取
信息抽取
关系(数据库)
数据集
深度学习
试验装置
训练集
机器学习
数据挖掘
自然语言处理
情报检索
万维网
梅德林
色谱法
化学
生物化学
程序设计语言
作者
Nada GabAllah,Ahmed Rafea
出处
期刊:Advances in intelligent systems and computing
日期:2022-01-01
卷期号:: 157-165
标识
DOI:10.1007/978-3-031-14054-9_16
摘要
AbstractInformation extraction from textual data is becoming more crucial with the increase of available data on the internet. Automatic extraction of information from biomedical data is very useful to researchers, saving time and effort exerted by them. Relation extraction between medical entities is one of the active research areas. In this paper we are presenting a relation extraction deep learning model based on SciBERT, to extract relations between drugs/chemicals and proteins/genes entities from PubMed literature. The model could achieve an average micro F1 score of 91.75% on the ChemProt test set.KeywordsRelation extractionDrug protectionBiomedicalDeep learningSciBERT
科研通智能强力驱动
Strongly Powered by AbleSci AI