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Recursively Binary Modification Model for Nested Named Entity Recognition

AAAI 2020 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add modification probabilities (head inference) (ablation)RBM · GENIAF1 (%): 77.7 -> 79.8Helped
add modification probabilities (head inference) (ablation)RBM · ACE2005F1 (%): 72.8 -> 73.6Helped
add pretrained embeddings (ablation)RBM · GENIAF1 (%): 74.4 -> 79.8Helped
add pretrained embeddings (ablation)RBM · ACE2005F1 (%): 71.5 -> 73.6Helped
add character-level embeddings (ablation)RBM · GENIAF1 (%): 66.1 -> 79.8Helped
add character-level embeddings (ablation)RBM · ACE2005F1 (%): 66.6 -> 73.6Helped
replace span-average representation with RBM head-based typing (ablation)Sohrab and Miwa span model · GENIAF1 (%): 77.1 -> 79.8Helped
RBM vs span-average on nested entities (ablation)Sohrab and Miwa span model · GENIA nested entitiesF1 (%): 72.7 -> 83.1Helped
RBM vs span-average on single-token entities (ablation)Sohrab and Miwa span model · GENIA single-tokenF1 (%): 69.9 -> 81.8Helped
RBM vs span-average on CELL-TYPE category (ablation)Sohrab and Miwa span model · GENIA CELL-TYPEF1 (%): 78.1 -> 76.8Hurt
RBM vs span-average on DNA category (ablation)Sohrab and Miwa span model · GENIA DNAF1 (%): 71.8 -> 75.0Helped
lower undersampling rate m=15 instead of 25 (ablation)RBM · ACE2005F1 (%): 73.6 -> 73.4No clear effect

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