Semi-supervised Relation Extraction with Large-scale Word Clustering

We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When training on different sizes of data, our semi-supervised approach consistently outperformed a state-of-the-art supervised baseline system.

• Type: Journal
• Year: 2011
• Where: ACL
• Authors: Ang Sun, Ralph Grishman, Satoshi Sekine
• Keywords: ACL 2011, Baseline System, Computational Linguistics, Granularity, Statistical Methods