Chinese named entity identification using cascaded hidden Markov model

An approach for Chinese named entity identification using cascaded hidden Markov model, which aimed to incorporate person name, location name, organization name recognition into an integrated theoretical frame was presented. Simple named entity was recognized by lower HMM model after rough segmentation and complex named entity such as person name, location name and organization name was recognized by higher HMM model using role tagging. In the test on large realistic corpus, its F-l measure of person name, location name and organization name was 92.55%, 94.53% and 86.51%. In the first international word segmentation bakeoff held by SIGHAN (the ACL Special Interest Group on Chinese Language Processing) in 2003. ICTCLAS, which name entity identification base on this model achieved excellent score.