Multi-grained Attention Network for Aspect-Level Sentiment Classification (D18-1)
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| Challenge: | Existing approaches to aspect sentiment classification use coarse-grained attention mechanisms . a novel approach captures word-level interaction between aspect and context . |
| Approach: | They propose a novel multi-grained attention network model for aspect level sentiment classification . they use a fine-grounded attention mechanism to capture word-level interaction between aspect and context . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on three datasets . it shows that aspect-level interactions can bring extra useful information and improve performance . |
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| Challenge: | Aspect-level sentiment classification aims to detect the sentiment polarity of a given opinion target in a sentence. |
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| Challenge: | Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features. |
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| Challenge: | Existing methods for aspect-specific sentiment classification are noisy and downgraded performance. |
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| Challenge: | Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. |
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