Construction of Condition Random Field Model Base Oriented to Word Sense Disambiguation
Yangsen Zhang · Jisuanji gongcheng · 2012
Taking Condition Random Field(CRF) method as probability model for word sense disambiguation model base,this paper uses CRF to train model file of high-frequency meaning and low-frequency meaning.It analyzes the probabilities in the result,determines a threshold to justify whether the marked tag is right,and uses the best performed model and its corresponding threshold to build CRF model base.Experimental results prove that model files of low-frequency meaning have better performance.The accuracy rate can reach above 80%,and the call rate is high.