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A Lightweight Neural Model for Biomedical Entity Linking

AAAI 2021 · Architectures and efficiency › Attention and transformers · approach: Language model pretraining and induction · 33 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the lightweight alignment-based ranking model instead of BERT-based RankingBiomedical entity linking · ShARe/CLEF, NCBI, ADRaccuracy: 90.10$\pm$1.00 vs 91.06$\pm$0.96; 89.07$\pm$2.63 vs 89.06$\pm$2.63; 92.63$\pm$0.81 vs 93.22$\pm$0.79No clear effect
Add character-level features (ablation)Our Base Model · ShARe/CLEF, NCBI, ADRaccuracy: -1.21; -0.31; -0.30Helped
Add the alignment layer (ablation)Our Base Model · ShARe/CLEF, NCBI, ADRaccuracy: -3.80; -4.06; -3.17Helped
Add the CNN layer (ablation)Our Base Model · ShARe/CLEF, NCBI, ADRaccuracy: -1.87; -0.93; -0.35Helped
Add mention-entity prior (ablation)Our Base Model · ShARe/CLEF, NCBI, ADRaccuracy change: +0.33; +0.04; +0.03Helped
Add context features (ablation)Our Base Model · ShARe/CLEF, NCBI, ADRaccuracy change: -0.09; +0.21; -0.24Mixed
Add entity-coherence features (ablation)Our Base Model · ShARe/CLEF, NCBI, ADRaccuracy change: -0.02; +0.27; +0.11Helped
Change word ordering in mention namesOur Base Model · Simulated ADR datasetsaccuracy: 92.63 -> 92.20; 92.63 -> 92.18; 92.63 -> 91.95; 92.63 -> 92.31; 92.63 -> 92.05No clear effect
Add typos to mention namesOur Base Model · Simulated ADR datasetsaccuracy: 92.63 -> 92.03; 92.63 -> 91.61; 92.63 -> 91.38; 92.63 -> 91.41; 92.63 -> 91.13Hurt
Use the alignment-based lightweight model instead of BERT modelsBiomedical entity linking · Average inference timeinference speed: 6.4x fasterHelped

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