基于特征的汉语词性标注模型  

A Feature-Based Chinese POS Tagging Model

作　　者：屈刚[1] 陆汝占[1] 

机构地区：[1]上海交通大学计算机科学与工程系,上海200030

出　　处：《计算机研究与发展》2003年第4期556-561,共6页Journal of Computer Research and Development

基　　金：国家"八六三"高技术研究发展计划项目基金 (863 3 0 6 ZT0 6 0 2 2 )

摘　　要：在隐马尔可夫模型的基础上提出了基于词汇特征的汉语词性标注模型 此模型不但考虑系统t时刻的状态 (词类 )对t+1时刻的状态的影响 ,还把t时刻的观察 (词 )对t+1时刻的状态的影响考虑进去 ,使模型更加精确 由于观察的数目较大 ,构造观察 状态转移概率矩阵的方法难以实用 ,于是给观察标以特征 ,并训练特征 状态转移概率矩阵 ,使概率矩阵占用较少的存储空间 。A feature based Chinese part of speech tagging model is presented, which is an extension of hidden Markov model This model not only takes the contribution of hidden state at time t into account, but also considers the contribution of the observation at time t+1 Since the number of observation is big, it is not practical to use the observation state matrix So in this paper each word is tagged with features, and then the matrix of feature state matrix is trained, which is much smaller The balance of accuracy and practicality is achieved

关 键 词：隐马尔可夫模型 词汇特征 汉语词性标注模型 自然语言处理 概率矩阵 中文信息处理 

分 类 号：TP391.12[自动化与计算机技术—计算机应用技术]