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, ngram 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.
Zhang et al. REF cast the name origin recognition problem as a multi-class classification task, by using a discriminative classification approach and extracting features from the names.
Name Origin Recognition Using Maximum Entropy Model and Diverse Features
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science