Research map / Label-efficient and robust learning / Semi-supervised learning
Acquiring Knowledge of Affective Events from Blogs Using Label Propagation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| label propagation over event-sentence graph instead of averaging sentence sentiment | AvgSent baseline · Affective events blogs, top460 | precision %: 80.0 -> 80.9 | Helped |
| add sentence-sentence edges between adjacent sentences (ablation) | GLOC · Affective events blogs, top460 | precision %: 80.9 -> 82.4 | Helped |
| add event-event co-occurrence edges (ablation) | GDIS · Affective events blogs, top460 | precision %: 82.4 -> 82.8 | Helped |
| add event-event co-occurrence edges (ablation) | GDIS · Affective events blogs, top100 | precision %: 88.0 -> 90.0 | Helped |
| add event-event co-occurrence edges (ablation) | GDIS · Affective events blogs, top300 | precision %: 84.3 -> 84.0 | Mixed |
| use document-level average sentiment instead of sentence-level | AvgSent baseline · Affective events blogs, top460 | precision %: 80.0 -> 71.5 | Hurt |
| label propagation GEV instead of sentiment lexicons | positive event polarity · Affective events, positive class | precision %: 64.3 -> 90.4 | Helped |
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