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AutoSense Model for Word Sense Induction

AAAI 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the target-neighbor pair constraint (ablation)AutoSense · SemEval 2010AVG: 23.36 -> 24.59Helped
Add the switch variable (ablation)AutoSense · SemEval 2010AVG: 22.92 -> 24.59Helped
Set the number of senses to 100 (ablation)AutoSense · SemEval 2010AVG: 24.59 -> 24.23Hurt
Tune the number of senses to 5AutoSense · SemEval 2010AVG: 24.59 -> 25.20Helped
Add the target-neighbor pair constraint (ablation)AutoSense · SemEval 2013AVG: 20.69 -> 22.16Helped
Add the switch variable (ablation)AutoSense · SemEval 2013AVG: 21.26 -> 22.16Helped
Set the number of senses to 100 (ablation)AutoSense · SemEval 2013AVG: 22.16 -> 21.03Hurt
Add actual additional contextsAutoSense · SemEval 2013AVG: 22.16 -> 24.37Helped
Increase the configured sense count from 5 to 100 (ablation)AutoSense · ArnetPaired F1: 56.2 -> 58.8Helped
Increase the configured sense count from 5 to 100 (ablation)AutoSense · PubMedPaired F1: 44.4 -> 46.5Helped
Increase the configured sense count from 5 to 100STM · PubMedPaired F1: 44.9 -> 41.9Hurt

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