An Improved Word Sense Disambiguation Method for Chinese Full-words Based on Unsupervised Learning
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摘要: 针对现存的基于EM (Expectation maximization)迭代的无指导词义消歧方法收敛缓慢、计算量大的问题, 利用互信息和Z-测试结合的方法选取特征, 并通过一种 统计学习算法估算初始参数值. 实验结果表明改进方法有效地提高了汉语词义消歧的准确率, 具有良好的扩展性和实用性.Abstract: The existing word sense disambiguation methods based on expectation maximization (EM) unsupervised learning need a large amount of computation and converge slowly. To address the problems, an improved method is proposed, which makes use of mutual information theory based on Z-test to select features and uses a statistical learning algorithm to estimate initial parameter values. The experimental result shows that the proposed method improves effectively the precision of word sense disambiguation and has good expansibility and practicability.
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