LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model-Reference-Cited by-同舟云学术

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LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model

Published:2021 Issue: Volume:9 Page:115563-115578
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shim Heereen,Lowet Dietwig,Luca Stijn,Vanrumste Bart

Funder

European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09503416.pdf?arnumber=9503416

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. From online reviews to smartwatch recommendation: An integrated aspect-based sentiment analysis framework;Journal of Retailing and Consumer Services;2025-01

2. Optimized neural attention mechanism for aspect-based sentiment analysis framework with optimal polarity-based weighted features;Knowledge and Information Systems;2024-01-02

3. A novel framework for aspect based sentiment analysis using a hybrid BERT (HybBERT) model;Multimedia Tools and Applications;2023-11-21

4. An Imperial Analysis of Large Language Models for Automated Tweet Sentiment Prediction;2023 International Conference on Sustainable Communication Networks and Application (ICSCNA);2023-11-15

5. BLIP-NLP Model for Sentiment Analysis;2023 2nd International Conference on Edge Computing and Applications (ICECAA);2023-07-19

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