Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Stratego/XT 0.16: components for transformation systems
Proceedings of the 2006 ACM SIGPLAN symposium on Partial evaluation and semantics-based program manipulation
Program Transformation with Scoped Dynamic Rewrite Rules
Fundamenta Informaticae - Program Transformation: Theoretical Foundations and Basic Techniques. Part 2
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Context-sensitive languages such as or can be parsed using a context-free but ambiguous grammar, which requires another stage, disambiguation, in order to select the single parse tree that complies with the language's semantical rules. Naturally, large and complex languages induce large and complex disambiguation stages. If, in addition, the parser should be extensible, for instance to enable the embedding of domain specific languages, the disambiguation techniques should feature traditional software-engineering qualities: modularity, extensibility, scalability and expressiveness. We evaluate three approaches to write disambiguation filters for SDF grammars: algebraic equations with ASF, rewrite-rules with programmable traversals for Stratego, and attribute grammars with TAG (TransformersAttribute Grammar), our system. To this end we introduce Phenix, a highly ambiguous language. Its ''standard'' grammar exhibits ambiguities inspired by those found in the and standard grammars. To evaluate modularity, the grammar is layered: it starts with a small core language, and several layers add new features, new production rules, and new ambiguities.