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Improving Neural Fine-Grained Entity Typing With Knowledge Attention

AAAI 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 66 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use mention attention instead of semantic attentionKNET · WIKI-AUTOmicro-F1: 74.91 -> 75.72Helped
use knowledge attention instead of semantic attentionKNET · WIKI-AUTOmicro-F1: 74.91 -> 76.22Helped
add entity disambiguation to knowledge attention (ablation)KNET · WIKI-AUTOmicro-F1: 76.22 -> 77.92Helped
add entity disambiguation to knowledge attention (ablation)KNET · WIKI-MANmicro-F1: 71.67 -> 75.32Helped
add entity disambiguation to knowledge attention (ablation)KNET · WIKI-AUTO strict accuracyaccuracy: 45.49 -> 47.20Helped
use knowledge attention instead of mention attention (ablation)KNET · WIKI-MANmicro-F1: 72.08 -> 71.67Hurt
add disambiguation to knowledge attention on erroneously disambiguated subset (ablation)KNET · WIKI-AUTO Erroneous subsetmicro-F1: 59.42 -> 57.56Hurt
add disambiguation to knowledge attention on correctly disambiguated subset (ablation)KNET · WIKI-AUTO Correct subsetmicro-F1: 79.83 -> 82.33Helped
raise disambiguation confidence threshold alpha (ablation)KA+D · validation setmicro-F1Mixed

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