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

ACL ID P03-1028
Title Closing The Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

In this paper, we present a learning ap- proach to the scenario template task of information extraction, where information filling one template could come from mul- tiple 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 analy- sis 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 re- search to have demonstrated that a learn- ing approach to the full-scale informa- tion extraction task could achieve per- formance rivaling that of the knowledge- engineering approach.