Bayesian Networks is a model that efficiently represents knowledge and probabilistic inference and is a popular graphics decision-making analysis tool.

贝叶斯网络是在不确定性环境下有效的知识表示方式和概率推理模型，是一种流行的图形决策化分析工具。

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Concerning the difficulties in part-of-speech tagging in English present participle, the authors analyzed the drawbacks of Hidden Markov Models (HMM) and proposed Bayesian decision tree model.

针对英文现在分词词性标注这一特定问题存在的难点分析了隐马尔可夫模型(HMM)的不足，提出了贝叶斯决策树模型。

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Results We demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision.

另外，结果显示：对于同样的数据不同的选择标准经常选择出不同的最优模型；

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