We examined the under-studied problem of semantic subtype induction, which involves classifying an NP as one of 92 semantic classes, and showed that two techniques -hierarchical classification and collective classificationcan significantly improve a baseline classification model trained using an off-the-shelf learning algorithm on the BBN Entity Type Corpus.
collective classification addresses a major weakness of the standard feature-based learning paradigm, where a classification model classifies each instance independently, failing to capture the relationships among subsets of instances that might improve classification accuracy.