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Traditional language processing tools constrain language designers tospecific kinds of grammars. In contrast, model-based language processing toolsdecouple language design from language processing. These tools allow theoccurrence of lexical and syntactic ambiguities in language specifications andthe declarative specification of constraints for resolving them. As a result,these techniques require scanners and parsers able to parse context-freegrammars, handle ambiguities, and enforce constraints for disambiguation. Inthis paper, we present Lamb and Fence. Lamb is a scanning algorithm thatsupports ambiguous token definitions and the specification of custom patternmatchers and constraints. Fence is a chart parsing algorithm that supportsambiguous context-free grammars and the definition of constraints onassociativity, composition, and precedence, as well as custom constraints. Lamband Fence, in conjunction, enable the implementation of the ModelCC model-basedlanguage processing tool.

Scanning and Parsing Languages with Ambiguities and Constraints: The Lamb and Fence Algorithms