Correlation analysis of visual verbs´ subcategorization based on Pearson´s correlation coefficient

In the research of modern linguistics, word frame information, which is significant in the study of Chinese information processing, draws more researchers´ attention. Its distinction between word argument and auxiliaries plays an important part in the precision of syntactic analysis, elimination of semantic ambiguities and semantic role labeling. Therefore, the study of categorization frame information became a hot issue in the recent years. With the constant development of machine learning technology, an increasing number of computational methods have been applied to many fields, including text classification, language processing and semantic analysis, etc. This approach is the supplement and breakthrough to the traditional methods of linguistic study. In this paper, Pearson´s correlation coefficient, which can reflect the correlative information between two variables, is adopted to analyze the correlation between the frequency and functions of Chinese visual verbs. The result is that word frequency takes on positive correlations with the main functions of the word, though with certain differences in the degree of the correlation.