Instance-based natural language generation

  • Authors:
  • Sebastian Varges;Chris Mellish

  • Affiliations:
  • University of Edinburgh;University of Edinburgh

  • Venue:
  • NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
  • Year:
  • 2001

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Abstract

This paper presents a bottom-up generator that makes use of Information Retrieval techniques to rank potential generation candidates by comparing them to a data base of stored instances. We introduce two general techniques to address the search problem, expectation-driven search and dynamic grammar rule selection, and present the architecture of an implemented generation system called IGEN. Our approach uses a domain-specific generation grammar that is automatically derived from a semantically tagged treebank. We then evaluate the efficiency of our system.