A part-of-speech tagging method based on improved hidden Markov model

In order to defy the unrealistic assumption of the part-of-speech tagging method based on hidden Markov models that successive observations are independent and identically distributed within a state,Markov family model(MFM) was introduced.Independence assumption in HMM was placed by conditional independence assumption in MFM.Markov Family model was applied to part-of-speech tagging,and syntactic parsing was combined with part-of-speech tagging.The part-of-speech tagging experiments show that Markov family models(MFMs) have higher performance than hidden.From the view of the statistics,the assumption of independence is stronger than the assumption of conditional independence,so language model based on MFM is more realistic than HMM language mode.Markov models(HMMs) under the same testing conditions,the precision is enhanced from 94.642% to 97.126%.