Model-based language specification has applications in the implementation of language processors, the design of domain-specific languages, model-driven software development, data integration, text mining, natural language processing, and corpus-based induction of models. Model-based language specification decouples language design from language processing and, unlike traditional grammar-driven approaches, which constrain language designers to specific kinds of grammars, it needs general parser generators able to deal with ambiguities. In this paper, we propose Fence, an efficient bottom-up parsing algorithm with lexical and syntactic ambiguity support that enables the use of model-based language specification in practice.
@article{arxiv.1107.4687,
title = {Fence - An Efficient Parser with Ambiguity Support for Model-Driven Language Specification},
author = {Luis Quesada and Fernando Berzal and Francisco J. Cortijo},
journal= {arXiv preprint arXiv:1107.4687},
year = {2011}
}