Probabilistic models for disambiguation of an HPSG-based chart generator

Abstract

We describe probabilistic models for a chart generator based on HPSG. Within the research field of parsing with lexicalized grammars such as HPSG, recent developments have achieved efficient estimation of probabilistic models and high-speed parsing guided by probabilistic models. The focus of this paper is to show that two essential techniques - model estimation on packed parse forests and beam search during parsing - are successfully exported to the task of natural language generation. Additionally, we report empirical evaluation of the performance of several disambiguation models and how the performance changes according to the feature set used in the models and the size of training data.