Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health (D19-55)
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| Challenge: | Existing weakly supervised learning frameworks are used for segment classification . lack of segment labels prevents the use of standard supervised methods . |
| Approach: | They propose a model that uses weak supervision to train supervised models for segment-level classification . they propose sigmoid attention mechanism-based aggregation function to improve the model . |
| Outcome: | The proposed model outperforms state-of-the-art models for segment-level sentiment classification by 9.8% in F1 . |
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| Challenge: | Existing methods for aspect-based sentiment analysis of review text use only a few keywords describing each aspect/sentiment without using any labeled examples. |
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