  Entity linking aims to establish a link between entity mentions in a document
and the corresponding entities in knowledge graphs (KGs). Previous work has
shown the effectiveness of global coherence for entity linking. However, most
of the existing global linking methods based on sequential decisions focus on
how to utilize previously linked entities to enhance the later decisions. In
those methods, the order of mention is fixed, making the model unable to adjust
the subsequent linking targets according to the previously linked results,
which will cause the previous information to be unreasonably utilized. To
address the problem, we propose a novel model, called DyMen, to dynamically
adjust the subsequent linking target based on the previously linked entities
via reinforcement learning, enabling the model to select a link target that can
fully use previously linked information. We sample mention by sliding window to
reduce the action sampling space of reinforcement learning and maintain the
semantic coherence of mention. Experiments conducted on several benchmark
datasets have shown the effectiveness of the proposed model.
