Graph mutual reinforcement based bootstrapping

Abstract

In this paper, we present a new bootstrapping method based on Graph Mutual Reinforcement (GMR-Bootstrapping) to learn semantic lexicons. The novelties of this work include 1) We integrate Graph Mutual Reinforcement method with the Bootstrapping structure to sort the candidate words and patterns; 2) Pattern's uncertainty is defined and used to enhance GMR-Bootstrapping to learn multiple categories simultaneously. Experimental results on MUC4 corpus show that GMR-Bootstrapping outperforms the state-of-the-art algorithms. We also use it to extract names of automobile manufactures and models from Chinese corpus. It achieves good results too. © 2008 Springer-Verlag Berlin Heidelberg.