Fence - An Efficient Parser with Ambiguity Support for Model-Driven Language Specification

TL;DR

Fence is a novel bottom-up parser designed to efficiently handle lexical and syntactic ambiguities, facilitating practical use of model-based language specifications across various domains.

Contribution

The paper introduces Fence, an efficient parser that supports ambiguity, enabling broader adoption of model-based language specification methods.

Findings

Fence effectively handles ambiguous language specifications.

The parser demonstrates high efficiency in processing ambiguous models.

Application examples show practical benefits in language processing tasks.

Abstract

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.