| Autor: |
Yu-Chih Deng, Yih-Ru Wang, Sin-Horng Chen, Lung-Hao Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 109974-109982 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3322436 |
| Popis: |
BERT (Bidirectional Encoder Representations from Transformers) uses an encoder architecture with an attention mechanism to construct a transformer-based neural network. In this study, we develop a Chinese word-level BERT to learn contextual language representations and propose a transformer fusion framework for Chinese sentiment intensity prediction in the valence-arousal dimensions. Experimental results on the Chinese EmoBank indicate that our transformer-based fusion model outperforms other neural-network-based, regression-based and lexicon-based methods, reflecting the effectiveness of integrating semantic representations in different degrees of linguistic granularity. Our proposed transformer fusion framework is also simple and easy to fine-tune over different downstream tasks. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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