Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
Table 1: Comparing entity linking results on CoNLL’03/AIDA. strong F1 and weak F1 denote InKB F1 scores. ED is Precision@1 for InKB. Kolitsas et al. (2018) also study a neural model, however, they only model MD and ED. The independent baseline shows how their model performs when they use mentions detected by Stanford NLP. In Frozen-BERT+Entity BERT is not trained and only the entity classifier on-top is trained.
['[EMPTY]', '[EMPTY]', 'AIDA/testa strong F1', 'AIDA/testa weak F1', 'AIDA/testa ED', 'AIDA/testb strong F1', 'AIDA/testb weak F1', 'AIDA/testb ED']
we compare our results to the most recent results by Kolitsas et al. They also provide a baseline in which they show how their classifier performs when MD and ED are independent, i.e. linking mentions detected by Stanford NLP.