Kazakh part-of-speech tagging method based on maximum entropy
Sang Haiya · Computer Engineering and Applications Journal · 2013
Maximum entropy model can make full use of context, agilely take multiple characteristics. This paper uses maximum entropy model to part of speech tagging of Kazakh, designs feature template according to tackiness and rich shape, and joins the backward relying part of speech feature template. In this paper, the module is improved, which takes the previous n words of highest probability to join the characteristic vector of next word and so on until the end of the sentence, and finally it selects a probability optimal sequence of part of speech tagging. The results show that feature template choice is correct, and the improved model accuracy rate reaches 96.8%.