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

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dc.creatorNgai, Grace
dc.creatorYarowsky, David
dc.date2001-05-02
dc.date.accessioned2026-07-07T03:17:07Z
dc.date.available2026-07-07T03:17:07Z
dc.descriptionThis 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.
dc.description9 pages, 4 figures, appeared in ACL2000
dc.identifierhttps://arxiv.org/abs/cs/0105003
dc.identifierhttp://arxiv.org/abs/cs/0105003
dc.identifierProceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pages 117-125, Hong Kong (2000)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30600
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.7
dc.titleRule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking
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