Identifying Chinese personal names based on Support Vector Machines and transformation-based error-driven learning

Recognition of Chinese personal name is emphasis and difficulty for unknown words recognition. If the problem is effectively solved, then it will improve the precision of unknown words recognition. The paper presents a method of Chinese name recognition based on Support Vector Machines (SVM) and transformation-based error-driven learning. Using the transformation-based learning approach to correct the identification results of SVM. Transformation rules effectively deal with the special cases of language phenomenon and improve the performance of SVM. Experiments show that the method is efficient in identifying person names from Chinese texts. In open test, the precision, recall, and F-measure are improved.