¹College of Information Technology, Jilin Agricultural University, Changchun 130118,China
²School of Optical and Electronic Engineering, Changchun University of Science and Technology, Changchun 130022,China
³cCollege of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China

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Abstract—Aiming at the situation of current Chinese language resources shortage ,this paper proposes semantically selectional preferences of unsupervised learning method, and presents a strategy of obtaining verbnoun semantic collocation in Chinese. An approach of Chinese semantic preference learning, which is based on Latent Semantic Clustering model and Expectation Maximization Algorithm. First, the parameters are initialized randomly. Second, a certain number of training iterations is performed until convergence. Each iteration consists of expectation step and maximization step. Finally, the semantic association between verbs and nouns are calculated as a measure of its matching probability. This method can be used on Chinese without syntax-annotated corpora. Lots of experiment results show that LSC provides proper patterns of verb-noun collocation semantically. The algorithm converges quickly.

Index Terms—selectional preferences, Latent Semantic Clustering(LSC), clustering selectional preferences, Expectation Maximization(EM) , unsupervised learning

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Cite: Dong-ming Li, Li-juan Zhang,Ming-quan Wang, Wei Su, "Chinese Learning of Semantical Selectional Preferences Based on LSC Model and Expectation Maximization Algorithm," Journal of Software vol. 7, no. 11, pp. 2488-2493, 2012.