CRYSTAL inducing a conceptual dictionary

  • Authors:
  • Stephen Soderland;David Fisher;Jonathan Aseltine;Wendy Lehnert

  • Affiliations:
  • Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA

  • Venue:
  • IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1995

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Abstract

One of the central knowledge sources of an information extraction (IE) system IS a dictionary of linguistic patterns that can be used to identify references to relevant information in a text Automatic creation of conceptual dictionaries is important for portability and scalability of an IE system This paper describes CRYSTAL, a system which automatically induces a dictionary of "concept-node definitions" sufficient to identify relevant information from a training corpus Each of these concept-node definitions is generalized as far as possible without producing errors, so that a minimum number of dictionary entries cover the positive training instances Because it tests the accuracy of each proposed definition, CRYSTAL can often surpass human intuitions in creating reliable extraction rules.