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pro vyhledávání: '"Li Zhanhuai"'
Publikováno v:
In Neural Networks November 2024 179
Publikováno v:
In Expert Systems With Applications 15 March 2024 238 Part E
Autor:
Nafa, Youcef, Chen, Qun, Chen, Zhaoqiang, Lu, Xingyu, He, Haiyang, Duan, Tianyi, Li, Zhanhuai
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 data. To alleviate the data labeling burden, Active Learning (AL) presen
Externí odkaz:
http://arxiv.org/abs/2012.12960
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 training data, and can not be easily tuned towards a target workload. Un
Externí odkaz:
http://arxiv.org/abs/2012.03513
Publikováno v:
In Neural Networks January 2024 169:475-484
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 analysis problem -- predicting and interpreting which entity pairs ar
Externí odkaz:
http://arxiv.org/abs/1912.02947
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. Unfortunately, high-quality labeled training
Externí odkaz:
http://arxiv.org/abs/1906.02502
Publikováno v:
In Knowledge-Based Systems 25 January 2023 260
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 for ER were built on a variety of learning models (most notably deep neu
Externí odkaz:
http://arxiv.org/abs/1810.12125
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 human in the resolution process, most notably the crowdsourcing approach
Externí odkaz:
http://arxiv.org/abs/1805.12502