关键词: 词义归纳,距离度量,高斯混合模型,混合聚类

Abstract: Word sense induction is an important topic in solving knowledge acquisition of word sense,and the most widely used method to word sense induction is based on cluster analysis algorithm.By comparing K-Means clustering algorithm with EM clustering algorithm on the model of word sense induction,we proposed a new hybrid clustering algorithm by integrating distance metric and Gaussian mixture model,which combine the advantages of distance metric and data distributed computing in the two cluster algorithms respectively to mine the role of geometrical properties and normal distribution information of training data in clustering analysis and then improve the performance of performance of word sense model.Experimental results show that the hybrid clustering algorithm proposed in this paper is very effective to improve the performance of word sense induction model.

Key words: Word sense induction,Distance metric,Gaussian mixture model,Hybrid clustering