"EM: the Most Used, Feared, and Respected Learning Algorithm in Natural Language Processing Today"

8/28/1998: [time not recorded]
[location not recorded]

Abstract: I'll talk about how the EM algorithm changed my life. It's well known that statistical methods can help alleviate the knowledge acquisition bottleneck in NLP and expert systems. However, we often don't have the right kind of data to (say) train a decision tree. For example, what if we want to build a disambiguator for the word "bank", but we only have raw text to train on? The EM (estimation-maximization) algorithm gives direction in such cases. EM is not really an algorithm you can look up in a textbook, unfortunately. It's more like a way of approaching problems. So I'll give examples of how EM slices into a series of NLP and other problems, including some of our own.

Last updated: Mon Jun 19 17:44:06 2006