Bootstrapping Without the Boot

Jason Eisner, Damianos Karakos

Abstract: "Bootstrapping" methods for learning require a small amount of supervision to seed the learning process. We show that it is sometimes possible to eliminate this last bit of supervision, by trying many candidate seeds and selecting the one with the most plausible outcome. We discuss such "strapping" methods in general, and exhibit a particular method for strapping word-sense classifiers for ambiguous words. Our experiments on the Canadian Hansards show that our unsupervised technique is significantly more effective than picking seeds by hand (Yarowsky, 1995), which in turn is known to rival supervised methods.