A Collocation-Based WSD Model: RFR-SUM

Weiguang Qu, Zhifang Sui, Genlin Ji, Shiwen Yu, Junsheng Zhou

Abstract: In this paper, the concept of Relative Frequency Ratio (RFR) is presented to evaluate the strength of collocation. Based on RFR, a WSD Model RFR-SUM is put forward to disambiguate polysemous Chinese word sense. It selects 9 frequently used polysemous words as examples, and achieves the average precision up to 92:50% in open test. It has compared the model with Naïve Bayesian Model and Maximum Entropy Model. The results show that the precision by RFR-SUM Model is 5:95% and 4:48% higher than that of Naïve Bayesian Model and Max- imum Entropy Model respectively. It also tries to prune RFR lists. The results reveal that leaving only 5% important collocation information can keep almost the same precision. At the same time, the speed is 20 times higher.