In our lab meeting tomorrow, Anoop will discuss bootstrapping via graph propagation.
Here are the title and abstract:
Bootstrapping via Graph Propagation
Abstract: *In natural language processing, the bootstrapping algorithm introduced by David Yarowsky (25 years ago!) is a discriminative unsupervised learning algorithm that uses some seed rules to bootstrap a classifier (this is the ordinary sense of bootstrapping which is distinct from the Bootstrap in statistics). The Yarowsky algorithm works remarkably well on a wide variety of NLP classification tasks such as distinguishing between word senses and deciding if a noun phrase is an organization, location, or person.
Extending previous attempts at providing an objective function optimization view of Yarowsky, we show that bootstrapping a classifier from a small set of seed rules can be viewed as the propagation of labels between examples via features shared between them. This talk introduces a novel variant of the Yarowsky algorithm based on this view. It is a bootstrapping learning method which uses a graph propagation algorithm with a well defined per-iteration objective function that incorporates the cautious behaviour of the original Yarowsky algorithm.
The experimental results show that our proposed bootstrapping algorithm achieves state of the art performance or better on several different natural language data sets, outperforming other unsupervised methods such as the EM algorithm. We show that cautious learning is an important principle in unsupervised learning, however we do not understand it well, and we show that the Yarowsky algorithm can outperform or match co-training without any reliance on multiple views.*
Tuesday, Jan 28th, 09:30 a.m. TASC1 9408. Title: Bootstrapping via Graph Propagation