Paper: Bootstrapping Coreference Classifiers With Multiple Machine Learning Algorithms

ACL ID W03-1015
Title Bootstrapping Coreference Classifiers With Multiple Machine Learning Algorithms
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003
Authors

Successful application of multi-view co- training algorithms relies on the ability to factor the available features into views that are compatible and uncorrelated. This can potentially preclude their use on problems such as coreference resolution that lack an obvious feature split. To bootstrap coref- erence classifiers, we propose and eval- uate a single-view weakly supervised al- gorithm that relies on two different learn- ing algorithms in lieu of the two different views required by co-training. In addition, we investigate a method for ranking un- labeled instances to be fed back into the bootstrapping loop as labeled data, aiming to alleviate the problem of performance deterioration that is commonly observed in the course of bootstrapping.