Research map / Architectures and efficiency / RNNs and normalization
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add sentence-level attention (ablation) | LSTM + TA · SentiHood test | Macro F1: 61.07 -> 76.69 | Helped |
| add sentence-level attention (ablation) | LSTM + TA · SemEval-2015 | Macro F1: 71.71 -> 70.73; Sent. Acc 69.24 -> 74.28 | Mixed |
| target-level attention instead of target averaging (ablation) | TDLSTM · SentiHood test | Macro F1: 58.17 -> 61.07 | Helped |
| multi-hop memory network for sentence attention (ablation) | LSTM + TA + SA · SemEval-2015 | Macro F1: 70.73 -> 52.73 | Hurt |
| embed commonsense knowledge via Sentic LSTM gate (ablation) | LSTM + TA + SA · SentiHood test | Macro F1: 76.69 -> 78.18 | Helped |
| embed commonsense knowledge via Sentic LSTM gate (ablation) | LSTM + TA + SA · SemEval-2015 | Macro F1: 70.73 -> 76.44 | Helped |
| feed knowledge concepts as input features (ablation) | LSTM + TA + SA · SentiHood test | Macro F1: 76.69 -> 76.33 | No clear effect |
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