KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Table 7: Entity typing results on OpenEntity (%). All methods with PLMs use the BASE size.
['Model', 'P', 'R', 'F-1']
To identify the entity mentions of interest, we add two special tokens before and after the entity spans, and use the representation of the first special token as the feature in the final classification step. Note that KEPLER does not perform any kind of linking, yet ERNIE and KnowBERT use entity linking and entity embeddings, which gives them a specific edge in entity-centered tasks. Still, KEPLER performs the best among its counterparts.