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.
@article{arxiv.cs/0105003,
title = {Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking},
author = {Grace Ngai and David Yarowsky},
journal= {arXiv preprint arXiv:cs/0105003},
year = {2007}
}