卓越IT工程师系列教材 源自实践 服务教学

Named Entity Recognition Method Based on Co-training of Reinforcement Learning

东软集团董事长刘积仁教授推荐这是一套来自实践者的作品。对中国IT教育和软件产业实践应用更紧密结合愿景的期待,驱动了东软的员工组织起来完成了这套作品。他们力图将自己在教育和研发中的收获传播给自己的同事、IT教育和软件产业的同行,并与在蓬勃发展中的中国软件产业分享;他们企盼着中国未来的软件工程师们在一个更贴近实用化的环境中学习和掌握技术的价值。东软为他们的行动而感动和自豪。

Named entity recognition(NER)is a technique for extracting meaningful entities from unstructured big datasets.NER has a wide range of applications.An example of NER is advanced data analysis which extracts date,train,platform and other entity information from a large operation logs dataset produced by rail transit trains.In recent years,the reinforcement learning based method has become the mainstream method of solving this task.However,these algorithms rely heavily on manual labeling.The over-fitting problem may occur when the training set is small,and cannot achieve the expected generalization effect.In this paper,we propose a novel method,Reinforced Co-Training.With only small amount of labeled data,the performance of the named entity recognition model can be automatically improved by using a large amount of unlabeled data.We have experimented our framework on corpus in two different fields,the results show that the F1 value of our proposed method is increased by 10%,which proves the effectiveness and generality of the method in this paper.We also compared our method with the traditional co-training methods,the F1 value of our method is 5%higher than other methods,which shows that this method is more intelligent.