Name Origin Recognition Using Maximum Entropy Model and Diverse Features
Min Zhang, Chengjie Sun, Haizhou Li, Ai Ti Aw, Chew Lim Tan, Xiao-Long Wang · 2008
nus.edu.sg Name origin recognition is to identify the source language of a personal or location name. Some early work used either rulebased or statistical methods with single knowledge source. In this paper, we cast the name origin recognition as a multi-class classification problem and approach the problem using Maximum Entropy method. In doing so, we investigate the use of different features, including phonetic rules, n-gram statistics and character position information for name origin recognition. Experiments on a publicly available personal name database show that the proposed approach achieves an overall accuracy of 98.44 % for names written in English and 98.10 % for names written in Chinese, which are significantly and consistently better than those in reported work. 1