no code implementations • 23 Dec 2020 • Youcef Nafa, Qun Chen, Zhaoqiang Chen, Xingyu Lu, Haiyang He, Tianyi Duan, Zhanhuai Li
Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER.
no code implementations • 7 Dec 2020 • Zhaoqiang Chen, Qun Chen, Youcef Nafa, Tianyi Duan, Wei Pan, Lijun Zhang, Zhanhuai Li
Built on the recent advances on risk analysis for ER, the proposed approach first trains a deep model on labeled training data, and then fine-tunes it by minimizing its estimated misprediction risk on unlabeled target data.
no code implementations • 6 Dec 2019 • Zhaoqiang Chen, Qun Chen, Boyi Hou, Tianyi Duan, Zhanhuai Li, Guoliang Li
Machine-learning-based entity resolution has been widely studied.
no code implementations • 6 Jun 2019 • Yanyan Wang, Qun Chen, Jiquan Shen, Boyi Hou, Murtadha Ahmed, Zhanhuai Li
The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA) were built on a variety of deep neural networks (DNN), whose efficacy depends on large amounts of accurately labeled training data.
no code implementations • 29 Oct 2018 • Boyi Hou, Qun Chen, Yanyan Wang, Youcef Nafa, Zhanhuai Li
Using ER as a test case, we demonstrate that gradual machine learning is a promising paradigm potentially applicable to other challenging classification tasks requiring extensive labeling effort.