"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.
The above is joint work with Damianos Karakos. If time permits, I will also discuss the use of dynamic programming techniques in text analysis, and the instantiation of such techniques in a new
declarative programming language, Dyna.
Audio (MP3 File, Podcast Ready)
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