Toward General-Purpose Learning for Information Extraction

  title={Toward General-Purpose Learning for Information Extraction},
  author={Dayne Freitag},
Two trends are evident in the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broadening of the central problem definition to include many non-traditional text domains. This development calls for information extraction systems which are as retctrgetable and general as possible. Here, we describe SRV, a learning architecture for information extraction which is designed for maximum… CONTINUE READING
Highly Influential
This paper has highly influenced 13 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 108 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 68 extracted citations

109 Citations

Citations per Year
Semantic Scholar estimates that this publication has 109 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-7 of 7 references

Machine Learning

  • T. M. Mitchell.
  • The
  • 1997
1 Excerpt

FASTUS: a nite-state processor

  • M. Tyson
  • 1993

Similar Papers

Loading similar papers…