no code implementations • 8 Apr 2024 • Murtadha Ahmed, Qun Chen
Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence.
no code implementations • 28 Jul 2023 • Na Chen, Xianming Kuang, Feiyu Liu, Kehao Wang, Qun Chen
Specifically, our proposed solution extracts indicative feature representations by deep backbones, and then constructs both unary and binary factors based on the extracted features to facilitate gradual learning.
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.