Rule writing or annotation: cost-efficient resource usage for base noun phrase chunking

  • Authors:
  • Grace Ngai;David Yarowsky

  • Affiliations:
  • Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD

  • Venue:
  • ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2000

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Abstract

This paper presents a comprehensive empirical comparison between two approaches for developing a base noun phrase chunker: human rule writing and active learning using interactive real-time human annotation. Several novel variations on active learning are investigated, and underlying cost models for cross-modal machine learning comparison are presented and explored. Results show that it is more efficient and more successful by several measures to train a system using active learning annotation rather than hand-crafted rule writing at a comparable level of human labor investment.