Exploiting auxiliary distributions in stochastic unification-based grammars

TL;DR

This paper introduces a method for estimating conditional probabilities in unification-based grammars that leverages auxiliary distributions, enhancing the incorporation of lexical preferences into stochastic grammatical models.

Contribution

It presents a general approach to utilize auxiliary distributions in stochastic unification-based grammars, demonstrated on a lexical-functional grammar.

Findings

Effective integration of auxiliary distributions improves parse probability estimation.

Method applicable to various stochastic grammar frameworks.

Enhances modeling of lexical selectional preferences.

Abstract

This paper describes a method for estimating conditional probability distributions over the parses of ``unification-based'' grammars which can utilize auxiliary distributions that are estimated by other means. We show how this can be used to incorporate information about lexical selectional preferences gathered from other sources into Stochastic ``Unification-based'' Grammars (SUBGs). While we apply this estimator to a Stochastic Lexical-Functional Grammar, the method is general, and should be applicable to stochastic versions of HPSGs, categorial grammars and transformational grammars.