Bigram Chinese Word Segmentation Model Based on Bayesian Network
Hong Zhou · Jisuanji gongcheng · 2010
This paper proposes Chinese word segmentation model based on Bayesian network,which adopts better smoothing algorithm to achieves word sense disambiguation and automatic recognition of foreign/domestic person names together.Viterbi algorithm is used in the model,which is demonstrated to be more efficient in word segmentation under acceptable accuracy and recall rate.Experimental results show that precision rate is 99.68% and recall rate is 99.7% in close test,with the speed of 74 800 words per second.