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Entity Linking On Aida Conll

Metrics

Micro-F1 strong

Results

Performance results of various models on this benchmark

Model Name	Micro-F1 strong	Paper Title	Repository
ReLiK-Large	86.4	ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
Kolitsas et al. (2018)	82.4	End-to-End Neural Entity Linking
Kannan Ravi et al. (2021)	83.1	CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Peters et al. (2019)	73.7	Knowledge Enhanced Contextual Word Representations
Zhang et al. (2021)	85.8	EntQA: Entity Linking as Question Answering
Hoffart et al. (2011)	72.8	Robust Disambiguation of Named Entities in Text	-
SpEL-base (2023)	88.1	SpEL: Structured Prediction for Entity Linking
Broscheit (2019)	79.3	Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
FusionED	86.5	Entity Disambiguation via Fusion Entity Decoding	-
Martins et al. (2019)	81.9	Joint Learning of Named Entity Recognition and Entity Linking	-
De Cao et al. (2021a)	83.7	Autoregressive Entity Retrieval
ReFinED	84.0	ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
De Cao et al. (2021b)	85.5	Highly Parallel Autoregressive Entity Linking with Discriminative Correction
ReLiK-Base	85.3	ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
Févry et al. (2020b)	76.7	Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking	-
van Hulst et al. (2020)	80.5	REL: An Entity Linker Standing on the Shoulders of Giants
SpEL-large (2023)	88.6	SpEL: Structured Prediction for Entity Linking

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