Accession Number : ADA477513
Title : Efficient Training Methods for Conditional Random Fields
Descriptive Note : Doctoral thesis
Corporate Author : MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
Personal Author(s) : Sutton, Charles A.
Full Text : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA477513
Report Date : FEB 2008
Pagination or Media Count : 220
Abstract : Many applications require predicting not a just a single variable, but multiple variables that depend on each other. Recent attention has therefore focused on structured prediction methods. Especially popular have been conditional random fields (CRFs), which are graphical models of the conditional distribution over outputs given a set of observed features. Unfortunately, parameter estimation in CRFs requires repeated inference. Complex graphical structures are increasingly desired in practical applications, but training time often becomes prohibitive. In this thesis, I investigate efficient training methods for conditional random fields with complex graphical structure, focusing on local methods which avoid propagating information globally along the graph. First, I investigate piecewise training, which trains each of a model's factors separately. I present three views of piecewise training: as maximizing the likelihood in a so-called "node-split graph", as maximizing the Bethe likelihood with uniform messages, and as generalizing the pseudo-moment matching estimator of Wainwright, Jaakkola, and Willsky. Second, I propose piecewise pseudolikelihood, a hybrid procedure which "pseudolikelihood-izes" the piecewise likelihood, and is therefore more efficient if the variables have large cardinality. Finally, I explore training methods using beliefs arising from stopping BP before convergence. I propose a new schedule for message propagation and present suggestive results applying dynamic schedules to the system of equations that combine inference and learning. I also present two novel families of loopy CRFs, which appear as test cases throughout. First is the dynamic CRF, which combines the factorized state representation of dynamic Bayesian networks with the modeling flexibility of conditional models. The second of these is the skip-chain CRF, which models the fact that identical words are likely to have the same label, even if they occur far apart.
Descriptors : *STATISTICAL INFERENCE , *MATHEMATICAL PREDICTION , MATHEMATICAL MODELS , ALGORITHMS , THESES , RANDOM VARIABLES
Subject Categories : STATISTICS AND PROBABILITY
Distribution Statement : APPROVED FOR PUBLIC RELEASE