Personal Name Recognition Based on Categorized Linguistic Knowledge

Abstract

This paper proposes an integrated approach for personal name recognition (PNR) in Chinese by utilizing both statistical language models and categorized linguistic knowledge. Various formulas are proposed for calculating personal name credibility and context credibility for different types of personal names. Experiment is conducted on large-scale corpus to evaluate the approach and the F-1 scores has reached 98.85% and 92.73% respectively in close and open test.