A Hybrid Approach for Chinese Pronunciation-Translated Person Names Recognition

摘要：

Pronunciation-translated person names (PPN) bring ambiguities to Chinese word segmentation. In this paper, we regard PPN recognition as a binary classification problem. We propose a hybrid approach that combines Conditional Random Fields(CRF) model and Support Vector Machines(SVM) model for the task of recognizing PPN. The experiments show that the performance of the hybrid model is better than either the CRF model or the SVM model. With regard to the analyses of the results individually generated by the CRF model and the SVM model, we also apply some appropriate rules to the hybrid model in order to prune errors. According to our overall experiments, the hybrid method with rules achieves a high precision in the final results, which demonstrates that our hybrid model is effective.

作者: Yan Liang Yaoting Zhu

作者单位: College of Information Technical Science of Nankai University, Tianjin 300074 China College of Compu College of Information Technical Science of Nankai University, Tianjin 300074 China

会议类型: 国际会议

会议名称: 2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)

会议地点: 镇江

会议语种:英文

页码: 1305-1310

在线出版日期: 2008-07-07（万方平台首次上网日期，不代表论文的发表时间）