Besides, due to the sparse supervision problem, entity scoring is often unreliable, which in turn influences the delayed feedback estimation.
base on this intuition, we devise the reward function as follows: where E 0 is the set of known entities in root node, E is the set of new entities, SIM(e, E) is the similarity score of newly extracted entity e to known entities, σ(•) is the sigmoid function, and a is a "temperature" hyperparameter.