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2003

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

8 years 11 months ago
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.
Hai Leong Chieu, Hwee Tou Ng, Yoong Keok Lee
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where ACL
Authors Hai Leong Chieu, Hwee Tou Ng, Yoong Keok Lee
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