Military Intelligence Nested Named Entity Recognition Based on Reinforcement Learning
Named entity recognition(NER)is a crucial task in military intelligence,especially chal-lenging when confronting nested entities and few-shot learning problems.For the above problems,a military intelligence nested named entity recognition based on reinforcement learning method is pro-posed.Firstly,used in a global pointer form,the start and end positions of an entity are regarded as a whole for recognition,and it is helpful to address the nested entity recognition problem.Then,through the optimization of the model's error learning strategy via reinforcement learning,the model can better learn with limited training samples.Finally,the concept of meta-learning is incorporated to assist the model in extracting general knowledge and patterns from a small number of tasks.Thus,the model's generalization capability is enhanced.Experimental results on a military intelligence dataset show that the effect of the method on extracting nested entities is greatly improved,compared with the existing baseline models.Its effectiveness and feasibility are proved.
named entity recognition(NER)reinforcement learningnested named entity recognition
万方数据
维普
