7 citations · 24 across the 9 of their papers we have counts for
9 papers
Active Deep Learning on Entity Resolution by Risk Sampling
Youcef Nafa, Qun Chen, Zhaoqiang Chen +4
While the state-of-the-art performance on entity resolution (ER) has been achieved by deep learning, its effectiveness depends on large quantities of accurately labeled training da…
Adaptive Deep Learning for Entity Resolution by Risk Analysis
Zhaoqiang Chen, Qun Chen, Youcef Nafa +4
The state-of-the-art performance on entity resolution (ER) has been achieved by deep learning. However, deep models are usually trained on large quantities of accurately labeled tr…
Towards Interpretable and Learnable Risk Analysis for Entity Resolution
Zhaoqiang Chen, Qun Chen, Boyi Hou +3
Machine-learning-based entity resolution has been widely studied. However, some entity pairs may be mislabeled by machine learning models and existing studies do not study the risk…
Gradual Machine Learning for Aspect-level Sentiment Analysis
Yanyan Wang, Qun Chen, Jiquan Shen +3
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 accurate…
Gradual Machine Learning for Entity Resolution
Boyi Hou, Qun Chen, Yanyan Wang +2
Usually considered as a classification problem, entity resolution (ER) can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions fo…
Improving Machine-based Entity Resolution with Limited Human Effort: A Risk Perspective
Zhaoqiang Chen, Qun Chen, Boyi Hou +2
Pure machine-based solutions usually struggle in the challenging classification tasks such as entity resolution (ER). To alleviate this problem, a recent trend is to involve the hu…