End-to-End Neural Entity Linking
Table 2: EL strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. We highlight the best and second best models, respectively. Training was done on AIDA-train set.
['[BOLD] F1@MA F1@MI', 'AIDA A', 'AIDA B', 'MSNBC', 'OKE-2015', 'OKE-2016', 'N3-Reuters-128', 'N3-RSS-500', 'Derczynski', 'KORE50']
At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets.