中南大学学报（自然科学版）2012，Vol.43Issue(8)：3053-3057,5.
基于改进的隐马尔科夫模型的词性标注方法
A part-of-speech tagging method based on improved hidden Markov model
摘要
Abstract
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 mode! (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 thaf 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%.关键词
隐马尔可夫模型/马尔可夫族模型/词性标注/Viterbi算法Key words
hidden Markov model/Markov family model/part-of-speech tagging/Viterbi algorithm分类
信息技术与安全科学引用本文复制引用
袁里驰..基于改进的隐马尔科夫模型的词性标注方法[J].中南大学学报（自然科学版）,2012,43(8):3053-3057,5.基金项目
国家自然科学基金资助项目(60763001) （60763001）
江西省自然科学基金资助项目(2010GZS0072) （2010GZS0072）
江西省教育厅科技项目(GJJ12271) （GJJ12271）
