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

@inproceedings{Chieu2003ClosingTG,
  title={Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods},
  author={Hai Leong Chieu and Hwee Tou Ng and Yoong Keok Lee},
  booktitle={ACL},
  year={2003}
}
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… CONTINUE READING
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Description of the UMass system as used for MUC-6

  • D. Fisher, S. Soderland, J. McCarthy, F. Feng, W. Lehnert.
  • Proceedings of MUC-6, pages 127–140.
  • 1995
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