Murtadha Ahmed, Qun Chen, Yanyan Wang, Youcef Nafa, Zhanhuai Li, and Tianyi Duan. 2021. DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis.
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis. Download PDF · Open Webpage · Murtadha H. M. Ahmed, Qun Chen, Yanyan Wang, Youcef Nafa ...
A Deep Neural Network (DNN) driven GML approach for ATSA is proposed, which exploits the power of DNN in feature representation for gradual learning and ...
... By gradual learning, GML can effectively bridge distribution alignment between labeled training data and unlabeled target data. GML has been successfully ...
Title: DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis ; Authors: Murtadha H. M. Ahmed, Qun Chen, Yanyan Wang, Youcef Nafa, Zhanhuai Li, ...
Jun 29, 2023 · In this paper, we propose a supervised GML approach for ATSA, which can effectively exploit labeled training data to improve knowledge conveyance.
Abstract. Recent work has shown that Aspect-Term Sen- timent Analysis (ATSA) can be effectively performed by Gradual Machine Learning.
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Apr 8, 2024 · we aim to leverage the strength of DNN in semantic relation modeling, which can facilitate effective knowledge transfer between labeled and ...
Abstract Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively performed by Gradual Machine Learning (GML).
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 ...