  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.
