A method of Chinese named entity recognition based on maximum entropy model

There are many connotative semantic features in Chinese which can help Chinese named entity recognition. Moreover, one of the important strongpoint of maximum entropy model is that it can syncretize features in different granularity and level. With that in mind, many Chinese named entity semantic knowledge bases were established by extracting information from corpus in this paper. However, because of the limitation of corpus´s size and data sparse which occurs universally in statistic-based method, much significant information can´t be extracted. In order to resolve this problem, in this thesis the idea of semantic expansion is applied in named entity recognition field. It is validated by experiment that relative to using unexpanded knowledge base average recall is increased by 1.17%, and F value is increased by 0.41%. Especially, the precision, recall and F value of complicated organization name recognition is increased by 0.24%, 1.39% and 0.86% respectively.