Inducing Features of Random Fields

TL;DR

This paper introduces a method for constructing complex random fields from training data by incrementally adding features supported by larger subgraphs, trained via divergence minimization and iterative algorithms.

Contribution

It presents a novel approach to learning non-Markovian random fields with many parameters, differing from traditional NLP and computer vision models, and demonstrates its application in word classification.

Findings

Effective feature construction for random fields.

Successful application to natural language word classification.

Comparison with decision trees and Boltzmann machines.

Abstract

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The statistical modeling techniques introduced in this paper differ from those common to much of the natural language processing literature since there is no probabilistic finite state or push-down automaton on which the model is built. Our approach also differs from the techniques common to the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing. Key words: random field, Kullback-Leibler divergence, iterative scaling, divergence geometry, maximum entropy, EM algorithm, statistical learning, clustering, word morphology, natural language processing