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1·Firstly, maximum a posteriori framework is created according to conditional random field model and Markov random field model.

根据条件随机场模型和马尔可夫随机场模型建立了一个最大后验概率框架。

2·So Conditional Random Field (CRF) is introduced to build POS tagging model in this paper, in order to overcome above problems.

论文引入条件随机域建立词性标注模型，易于融合新的特征，并能解决标注偏置的问题。

3·In our method, we transformed the problem into an equivalent sequence tagging problem, and built up the automatic generation model through the first order conditional random field.

我们的方法是将简称生成问题转化为等价的序列标注问题，并利用一阶条件随机场建立自动生成模型。

4·Secondly, this paper proposed a method combined rule-based pos selection model with Statistics-based Cascading Conditional random field to conduct semantic Role Labeling of Chinese Question.

其次，本文选用了一种将基于统计方法的条件随机场模型与基于规则方法的词性筛选模型相结合的方式对中文问句进行语义角色自动标注。

5·A regularized image restoration is the optimization for some conditional constraint, and the selection of wavelet coefficients based Bayesian statistic is on the image random field view.

正则化图像恢复是条件约束的最优化问题，而小波系数的贝叶斯统计选择是基于图像的随机场观点。