|Stochastic Systems Group|
Discriminative linear models for natural language processing
Recent work in machine learning approaches to natural language problems has considered discriminative methods such as log-linear (or maximum-entropy) models, the perceptron algorithm, and algorithms based on support vector machines.
In this talk I will describe some recent results in this area. In particular, I'll describe a method that generalizes support vector machine methods to supervised training of Markov random fields, hidden markov models, probabilistic context-free grammars, and other structured models. I will describe a new algorithm for solving the "large-margin" optimization problem defined in (Taskar, Guestrin and Koller 2003). The optimization method makes use of algorithms such as the forward-backward or inside-outside algorithm, and relies on the application of exponentiated gradient updates (Kivinen and Warmuth, 1997) to quadratic programs.
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