Deep neural network models for Uyghur personal pronouns resolution learn semantic information for current anaphora chain

but ignore the long-term effects of single anaphora chain recognition results. This paper proposes a Uyghur personal pronoun anaphora resolution based on deep reinforcement learning. This method defines the anaphora resolution task as the sequential decision process under the reinforcement learning environment

and effectively uses the antecedent information in the previous state to determine the current personal pronoun-candidate antecedent pairs. In this study

we use an overall reward signal optimization strategy

which is more efficient than directly using the loss fun

ction heuristic to optimize a specific single decision. Finally

we conduct experiments in the Uyghur dataset. The experimental results show that the

F

value of this method in the Uyghur personal pronouns resolution task is 85.80%. The experimental results show that the deep reinforcement learning model can significantly improve the performance of the Uyghur personal pronouns resolution.