End-to-End Neural Entity Linking
Table 3: AIDA A dataset: Gold mentions are split by the position they appear in the p(e|m) dictionary. In each cell, the upper value is the percentage of the gold mentions that were annotated with the correct entity (recall), whereas the lower value is the percentage of gold mentions for which our system’s highest scored entity is the ground truth entity, but that might not be annotated in the end because its score is below the threshold δ.
['[BOLD] solved% matched gold entity% position of ground truth in  [ITALIC] p( [ITALIC] e| [ITALIC] m)', 'Number of mentions', 'ED Global model', 'ED Base + Att model', 'ED Base model', 'EL Global model', 'EL Base + Att model', 'EL Base model', 'EL Global -log [ITALIC] p( [ITALIC] e| [ITALIC] m)']
One can see that the logp(e|m) prior is very helpful for correctly linking unambiguous mentions, but is introducing noise when gold entities are not frequent. For this category of rare entities, removing this prior completely will result in a significant improvement, but this is not a practical choice since the gold entity is unknown at test time.