Probabilistic Constraint Logic Programming

TL;DR

This paper introduces a probabilistic constraint logic programming model with algorithms for parameter estimation from incomplete data and for retrieving the most probable analyses, advancing probabilistic modeling in complex logical frameworks.

Contribution

It presents a log-linear probabilistic model for constraint logic programming and extends existing algorithms for parameter estimation and analysis retrieval to more expressive models.

Findings

Developed a log-linear probabilistic model for constraint logic programming

Extended the iterative scaling algorithm for parameter estimation in large data spaces

Proposed a method for finding most probable analyses in natural language processing

Abstract

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.