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

  title={Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods},
  author={Hai Leong Chieu and Hwee Tou Ng and Yoong Keok Lee},
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
Highly Cited
This paper has 52 citations. REVIEW CITATIONS
38 Citations
17 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 38 extracted citations

53 Citations

Citations per Year
Semantic Scholar estimates that this publication has 53 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 17 references

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
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…