Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking

Details Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking

2001-05-02

arxiv.org/abs/cs/0105003v1

Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pages 117-125, Hong Kong (2000)

Computer Science

Computation and Language

9 pages, 4 figures, appeared in ACL2000

Scientific paper

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.

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