Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods

Hai Leong Chieu, Hwee Tou Ng, Yoong Keok Lee

Abstract: In this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. When tested on the MUC-4 task, our learning approach achieves accuracy competitive to the best of the MUC-4 systems, which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. To our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledge engineering approach.