ID | 118516 |
著者 |
Ding, Fei
Tokushima University
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キーワード | Neuro-symbolic AI
Sentiment Analysis
Fine-tuned Transformer
Latent Dirichlet Allocation
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資料タイプ |
学術雑誌論文
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抄録 | For text sentiment analysis, state-of-the-art neural language models have demonstrated promising performance. However, they lack interpretability, require vast volumes of annotated data, and are typically specialized for tasks. In this paper, we explore a connection between fine-tuned Transformer models and unsupervised LDA approach to cope with text sentiment analysis tasks, inspired by the concept of Neuro-symbolic AI. The Transformer and LDA models are combined as a feature extractor to extract the hidden representations of the input text sequences. Subsequently, we employ a feedforward network to forecast various sentiment analysis tasks, such as multi-label emotion prediction, dialogue quality prediction, and nugget detection. Our proposed method obtains the best results in the NTCIR-16 dialogue evaluation (DialEval-2) task, as well as cutting-edge results in emotional intensity prediction using the Ren_CECps corpus. Extensive experiments show that our proposed method is highly explainable, cost-effective in training, and superior in terms of accuracy and robustness.
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掲載誌名 |
IEEE Transactions on Affective Computing
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ISSN | 19493045
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出版者 | IEEE
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巻 | 15
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号 | 2
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開始ページ | 493
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終了ページ | 507
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発行日 | 2023-05-23
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備考 | 論文本文は2025-05-23以降公開予定
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権利情報 | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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出版社版DOI | |
出版社版URL | |
言語 |
eng
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著者版フラグ |
その他
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部局 |
理工学系
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