A Model-Driven Probabilistic Parser Generator

TL;DR

This paper introduces a flexible, model-driven probabilistic parser generator that enhances disambiguation capabilities by integrating arbitrary statistical models, surpassing traditional grammar-based limitations.

Contribution

It presents a novel probabilistic parsing approach built on ModelCC, allowing for context-aware disambiguation and supporting complex references in syntax graphs.

Findings

Supports arbitrary probability estimators in parsing

Enables disambiguation of complex references

Demonstrates expressive power with a natural language parser

Abstract

Existing probabilistic scanners and parsers impose hard constraints on the way lexical and syntactic ambiguities can be resolved. Furthermore, traditional grammar-based parsing tools are limited in the mechanisms they allow for taking context into account. In this paper, we propose a model-driven tool that allows for statistical language models with arbitrary probability estimators. Our work on model-driven probabilistic parsing is built on top of ModelCC, a model-based parser generator, and enables the probabilistic interpretation and resolution of anaphoric, cataphoric, and recursive references in the disambiguation of abstract syntax graphs. In order to prove the expression power of ModelCC, we describe the design of a general-purpose natural language parser.