Chinese Named Entity Recognition Using Modified Conditional Random Field on Postal Address

Named entity recognition(NER) has been studied for a long time as more and more researches about the embedding, neural network model and some others systems like Language Model have developed quickly. However, as these systems rely heavily on domain-specific knowledge and it can hardly acquires much data about Chinese postal addresses, Chinese Named entity recognition(CNER) task on postal address has developed slowly. In this paper, we use a modified Conditional Random Field(CRF) model to solve a CNER task on a postal address corpus. Since there has little data about Chinese postal addresses and parts of which are incomplete sentences, we utilize the known, useful, clearer semantics words and sentences to our model as the additional features. We make three experiments to evaluate our system which obtains good performance and it shows that our modified algorithm performs better than other traditional algorithms when processing postal addresses.