Bootstrapping Large-scale Named Entities using URL-Text Hybrid Patterns

Automatically mining named entities (NE) is an important but challenging task, pattern-based and bootstrapping strategy is the most widely accepted solution. In this paper, we propose a novel method for NE mining using web document titles. In addition to the traditional text patterns, we propose to use url-text hybrid patterns that introduce url criterion to better pinpoint high-quality NEs. We also design a multiclass collaborative learning mechanism in bootstrapping, in which different patterns and different classes work together to determine better patterns and NE instances. Experimental results show that the precision of NEs mined with the proposed method is 0.96 and 0.94 on Chinese and English corpora, respectively. Comparison result also shows that the proposed method significantly outperforms a representative method that mines NEs from large-scale query logs.