SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media

作者：

Highlights：

• We develop a semi-supervised ensemble learning framework for adverse drug event (ADE) relation extraction.

• We propose two semi-supervised ensemble algorithms under the guidance of the SSEL-ADE framework.

• We develop six concrete semi-supervised ensemble methods under the guidance of the SSEL-ADE framework.

摘要

•We develop a semi-supervised ensemble learning framework for adverse drug event (ADE) relation extraction.•We propose two semi-supervised ensemble algorithms under the guidance of the SSEL-ADE framework.•We develop six concrete semi-supervised ensemble methods under the guidance of the SSEL-ADE framework.

论文关键词：Ensemble learning,Semi-supervised learning,Social media,Adverse drug event extraction

论文评审过程：Received 13 April 2017, Revised 28 August 2017, Accepted 15 October 2017, Available online 27 October 2017, Version of Record 5 February 2018.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.10.003