TitleA collocation-based WSD model: RFR-SUM
AuthorsQu, Weiguang
Sui, Zhifang
Ji, Genlin
Yu, Shiwen
Zhou, Junsheng
AffiliationPeking Univ, Inst Computat Logist, Beijing 100871, Peoples R China.
Issue Date2007
CitationNew Trends in Applied Artificial Intelligence, Proceedings.4570(23-32).
AbstractIn 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 Naive 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 Na:ive Bayesian Model and Maximum 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.
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