Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
| Autor: | Pradeep Karuturi, William Brendel, Luís Marujo, Jing Jiang, Jianfei Yu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Emotion classification 02 engineering and technology DUAL (cognitive architecture) computer.software_genre 020204 information systems Transfer (computing) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business Representation (mathematics) computer Sentence Natural language processing |
| Zdroj: | EMNLP |
| DOI: | 10.18653/v1/d18-1137 |
| Popis: | In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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