基于隐马尔可夫模型(HMM)的词性标注的应用研究

利用隐马尔可夫模型(HMM)对英语文本进行词性标注,首先介绍了对Viterbi算法的改进和基于HMM模型方法训练机器的步骤,然后通过一系列对比实验,得出两个结论:二元文法模型的“性能价格比”较三元文法模型更令人满意;词性标注集的个数对词性标注的准确率有影响。最后利用上述结论进行了封闭式测试和开放式测试。

Application Study of Hidden Markov Model Based Part-of-speech Tagging

This paper adopts Hidden Markov Model in part-of-speech tagging for English texts.Firstly the authors intro-duce the improvement on Viterbi algorithm and the step in training computer by means of hidden Markov model.Sec-ondly,through a series of contrast experiments the authors come to the conclusions:The Bigram model is better than the Trigram model in terms of the performance-cost ratio.The number of part-of-speech tagging set has an impact on the accuracy.Based on the conclusions mentioned above,the authors conduct closed and open tests,respectively.