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Utilizing External Knowledge to Enhance Semantics in Emotion Detection in Conversation
https://tokushima-u.repo.nii.ac.jp/records/2009468
https://tokushima-u.repo.nii.ac.jp/records/2009468526dfee7-0bcc-4184-b57d-2da18815b411
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
access_9_154947.pdf (5.35 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||
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| 公開日 | 2021-11-25 | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版社版DOI | ||||||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2021.3128277 | |||||||||||||
| 関連名称 | 10.1109/ACCESS.2021.3128277 | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Utilizing External Knowledge to Enhance Semantics in Emotion Detection in Conversation | |||||||||||||
| タイトル別表記 | ||||||||||||||
| その他のタイトル | Utilizing External Knowledge to Enhance Semantics in Emotion Detection | |||||||||||||
| 著者 |
任, 福継
× 任, 福継
WEKO
401
× She, Tianhao
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| 抄録 | ||||||||||||||
| 内容記述 | Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. We propose KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets. | |||||||||||||
| キーワード | ||||||||||||||
| 主題 | Affective computing | |||||||||||||
| キーワード | ||||||||||||||
| 主題 | text emotion classification | |||||||||||||
| キーワード | ||||||||||||||
| 主題 | emotion recognition in conversation | |||||||||||||
| 書誌情報 |
en : IEEE Access 巻 9, p. 154947-154956, 発行日 2021-11-15 |
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| 収録物ID | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 21693536 | |||||||||||||
| 出版者 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
| 権利情報 | ||||||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | |||||||||||||
| EID | ||||||||||||||
| 識別子 | 383188 | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
