ID | 115378 |
著者 |
呉, 雨濃
Tokushima University
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キーワード | Bayesian inference
emotion-topic model
emotion recognition
multi-label classification
natural language understanding
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資料タイプ |
学術雑誌論文
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抄録 | Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the word-level and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-the-art emotion prediction algorithms.
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掲載誌名 |
IEEE/CAA Journal of Automatica Sinica
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ISSN | 23299274
23299266
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cat書誌ID | AA1272385X
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出版者 | IEEE
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巻 | 5
|
号 | 1
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開始ページ | 204
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終了ページ | 216
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発行日 | 2017-01-25
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権利情報 | Open Access
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EDB ID | |
出版社版DOI | |
出版社版URL | |
フルテキストファイル | |
言語 |
eng
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著者版フラグ |
出版社版
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部局 |
理工学系
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