A Model-Driven Probabilistic Parser Generator
This work addresses parsing ambiguities for natural language processing applications, but appears incremental as it builds on an existing model-based parser generator.
The paper tackles the limitations of existing probabilistic scanners and parsers by proposing a model-driven tool that supports statistical language models with arbitrary probability estimators, enabling probabilistic interpretation and resolution of various references in abstract syntax graph disambiguation.
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.