A Sequence Kernel Method for Chinese Subcategorization Analysis

There have been a lot of researches focusing on large-scaled automatic acquisition of subcategorization frames, and many achievements have been made for lexicon building in quite a few languages, but subcategorization analysis for individual sentences still remains in a rarely touched field. This paper proposed to analyze Chinese subcategorization as a classification task by means of sequence kernel methods, which exploited the potential relations among the respective sentential constituents. Our final classification with word sequence kernel congregation and Part-of-speech (POS) sequence kernel C-support vector machine (C-SVM) achieved a very promising accuracy ratio of 92.36% on the testing set, which is 13.51% higher than the baseline performance of the existing Chinese subcategorization hypothesis generator.