Determining Gender of Korean Names with Context

Machine translation systems have various problems although they have been developed continuously. Especially, in Korean-English translation system, zero pronoun problem is an important problem, since omitted subject or object Korean are must be restored in English. In order to solve this problem, various methods have been proposed. In this paper, we focus on the gender determination problem in Korean names as a first-step for solving a zero pronoun problem in Korean. Since this problem can be viewed as a binary classification problem, we adopt support vector machines which are well-known for solving binary classification. The bag-of-words model is used to represent a name with context as a vector and information entropy of words is adopted for selecting features. An evaluation of the proposed method shows about 86% of accuracy. This method achieves higher accuracy than baseline which determines the gender of a name by its majority and additionally resolves the limitation of memory based and statistical method which use only names.