Research map / Label-efficient and robust learning / Recognition and long-tail
Gated Neural Networks for Targeted Sentiment Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add bi-directional GRNN layer before pooling (ablation) | pooling baseline · Targeted sentiment test | accuracy: 65.13 -> 69.65 | Helped |
| add three-way gated context interaction (G3) (ablation) | pooling baseline · Targeted sentiment test | accuracy: 65.13 -> 67.79 | Helped |
| add G3 on top of GRNN (ablation) | baseline+GRNN · Targeted sentiment test | accuracy: 69.65 -> 71.96 | Helped |
| add gates to recurrent layer (GRNN vs RNN) (ablation) | baseline+RNN · Targeted sentiment dev | accuracy: 68.05 -> 71.43 | Helped |
| add plain bi-directional RNN (ablation) | pooling baseline · Targeted sentiment dev | accuracy: 65.73 -> 68.05 | Helped |
| gated model vs Vo and Zhang (2015) with lexicons | targeted sentiment · Targeted sentiment test | macro F1: 65.56 -> 69.55 | Helped |
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- SNNN: Promoting Word Sentiment and Negation in Neural Sentiment Classification (AAAI 2018 · similarity 0.84)
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