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

Grace Ngai; David Yarowsky

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

Grace Ngai·David Yarowsky

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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.

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Grace Ngai, David Yarowsky. 2001-05-02. Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking. https://arxiv.org/abs/cs/0105003

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