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

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 MUC4 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 knowledgeengineering approach.

• Type: Conference
• Year: 2003
• Where: ACL
• Authors: Hai Leong Chieu, Hwee Tou Ng, Yoong Keok Lee
• Keywords: ACL 2003, ACL 2007, Approach Achieves Accuracy, Information Extraction, Scenario Template Task