Given a small number of coreference-annotated documents and a large number of unlabeled documents, these weakly supervised learners aim to incrementally augment the labeled data by iteratively training a classifier 1 on the labeled data and using it to label mention pairs randomly drawn from the unlabeled documents as COREFERENT or NOT COREFERENT.
classifying mention pairs using such iterative approaches is undesirable for coreference resolution: since the non-coreferent mention pairs significantly outnumber their coreferent counterparts, the resulting classifiers generally have an increasing tendency to (mis)label a pair as non-coreferent as bootstrapping progresses (see Ng and Cardie (2003)).