Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking
<|reference_start|>Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking: 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.<|reference_end|>
@article{ngai2001rule,
    title={Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun
  Phrase Chunking},
    author={Grace Ngai and David Yarowsky},
    journal={Proceedings of the 38th Annual Meeting of the Association for
  Computational Linguistics, pages 117-125, Hong Kong (2000)},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0105003},
    primaryClass={cs.CL cs.AI}
}