End-to-End Neural Entity Linking
Table 11: El strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. Training was done on Gerdaq train dataset. We highlight in red and blue the best and second best models, respectively.
['[BOLD] F1@MA F1@MI', 'GERDAQ-Dev', 'GERDAQ-Test', 'DBpediaSpotlight']
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.