Generation that exploits corpus-based statistical knowledge

  • Authors:
  • Irene Langkilde;Kevin Knight

  • Affiliations:
  • University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA

  • Venue:
  • COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
  • Year:
  • 1998

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Abstract

We describe novel aspects of a new natural language generator called Nitrogen. This generator has a highly flexible input representation that allows a spectrum of input from syntactic to semantic depth, and shifts the burden of many linguistic decisions to the statistical post-processor. The generation algorithm is compositional, making it efficient, yet it also handles non-compositional aspects of language. Nitrogen's design makes it robust and scalable, operating with lexicons and knowledge bases of one hundred thousand entities.