  Name disambiguation is a key and also a very tough problem in many online
systems such as social search and academic search. Despite considerable
research, a critical issue that has not been systematically studied is
disambiguation on the fly -- to complete the disambiguation in the real-time.
This is very challenging, as the disambiguation algorithm must be accurate,
efficient, and error tolerance. In this paper, we propose a novel framework --
CONNA -- to train a matching component and a decision component jointly via
reinforcement learning. The matching component is responsible for finding the
top matched candidate for the given paper, and the decision component is
responsible for deciding on assigning the top matched person or creating a new
person. The two components are intertwined and can be bootstrapped via jointly
training. Empirically, we evaluate CONNA on two name disambiguation datasets.
Experimental results show that the proposed framework can achieve a
1.21%-19.84% improvement on F1-score using joint training of the matching and
the decision components. The proposed CONNA has been successfully deployed on
AMiner -- a large online academic search system.
