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Your search keyword '"Zeng, Biqing"' showing total 42 results
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42 results on '"Zeng, Biqing"'

1. THE BAT: Thoughts Hierarchical Enhancement Beyond Arbitrary Text Style Transfer

Author

Zeng, Biqing, Liang, Junjie, Hua, Yining, Li, Ruizhe, Deng, Huimin, Peng, Yihao, Wang, Ruitang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Si, Zhanjun, editor, and Zhang, Chuanlei, editor

Published
2024
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4. Combating Medical Label Noise via Robust Semi-supervised Contrastive Learning

Author

Chen, Bingzhi, Ye, Zhanhao, Liu, Yishu, Zhang, Zheng, Pan, Jiahui, Zeng, Biqing, Lu, Guangming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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7. Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding

Author

Yang, Heng and Zeng, Biqing

Subjects
Computer Science - Computation and Language
Abstract

Target-oriented sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets. To improve the performance of sentiment classification, many approaches proposed various attention mechanisms to capture the important context words of a target. However, previous approaches ignored the significant relatedness of a target's sentiment and its local context. This paper proposes a local context-aware network (LCA-Net), equipped with the local context embedding and local context prediction loss, to strengthen the model by emphasizing the sentiment information of the local context. The experimental results on three common datasets show that local context-aware network performs superior to existing approaches in extracting local context features. Besides, the local context-aware framework is easy to adapt to many models, with the potential to improve other target-level tasks.

Published
2020

8. A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

Author

Yang, Heng, Zeng, Biqing, Yang, JianHao, Song, Youwei, and Xu, Ruyang

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the existing work focuses on the subtask of aspect term polarity inferring and ignores the significance of aspect term extraction. Besides, the existing researches do not pay attention to the research of the Chinese-oriented ABSA task. Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC. Compared with existing models, this model equips the capability of extracting aspect term and inferring aspect term polarity synchronously, moreover, this model is effective to analyze both Chinese and English comments simultaneously and the experiment on a multilingual mixed dataset proved its availability. By integrating the domain-adapted BERT model, the LCF-ATEPC model achieved the state-of-the-art performance of aspect term extraction and aspect polarity classification in four Chinese review datasets. Besides, the experimental results on the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets outperform the state-of-the-art performance on the ATE and APC subtask., Comment: Submitted to Elsevier

Published
2019

9. Ultra-short-Term Load Forecasting Model Based on VMD and TGCN-GRU

Author

Ding, Meirong, Zhang, Hang, Zeng, Biqing, Cai, Gaoyan, Chai, Yuan, Gan, Wensheng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fujita, Hamido, editor, Fournier-Viger, Philippe, editor, Ali, Moonis, editor, and Wang, Yinglin, editor

Published
2022
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42. Guided Discrimination and Correlation Subspace Learning for Domain Adaptation.

Author

Lu Y, Wong WK, Zeng B, Lai Z, and Li X

Abstract

As a branch of transfer learning, domain adaptation leverages useful knowledge from a source domain to a target domain for solving target tasks. Most of the existing domain adaptation methods focus on how to diminish the conditional distribution shift and learn invariant features between different domains. However, two important factors are overlooked by most existing methods: 1) the transferred features should be not only domain invariant but also discriminative and correlated, and 2) negative transfer should be avoided as much as possible for the target tasks. To fully consider these factors in domain adaptation, we propose a guided discrimination and correlation subspace learning (GDCSL) method for cross-domain image classification. GDCSL considers the domain-invariant, category-discriminative, and correlation learning of data. Specifically, GDCSL introduces the discriminative information associated with the source and target data by minimizing the intraclass scatter and maximizing the interclass distance. By designing a new correlation term, GDCSL extracts the most correlated features from the source and target domains for image classification. The global structure of the data can be preserved in GDCSL because the target samples are represented by the source samples. To avoid negative transfer issues, we use a sample reweighting method to detect target samples with different confidence levels. A semi-supervised extension of GDCSL (Semi-GDCSL) is also proposed, and a novel label selection scheme is introduced to ensure the correction of the target pseudo-labels. Comprehensive and extensive experiments are conducted on several cross-domain data benchmarks. The experimental results verify the effectiveness of the proposed methods over state-of-the-art domain adaptation methods.

Published
2023
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