Word-position-based tagging for Chinese word segmentation
Fan Xiao-zhong · Journal of Shandong University · 2010
The performance of Chinese word segmentation has been greatly improved by the word-position-based approaches in recent years.This approach treated Chinese word segmentation as a word-position tagging problem.With the help of a powerful sequence tagging model,the word-position-based method could quickly rose as a mainstream technique in this field.Feature template selection was crucial in this method.This technique was further studied via using four word-positions and conditional random fields.Closed evaluations were performed on corpus from the third and the fourth international Chinese word segmentation Bakeoff,and comparative experiments were performed on different feature templates.Experimental results showed that the feature template set:TMPT-10' was much better performance than the traditional template set.