AbstractAttribute grammars can be considered as an extension of context-free grammars, where the attributes are associated with grammar symbols, and the semantic rules define the values of the attributes. This formalism is widely applied for the specification and implementation of the compilation-oriented languages. The paper presents a method for learning semantic functions of attribute grammars which is a hard problem because semantic functions can also represent relations. The method uses background knowledge in learning semantic functions of S-attributed and L-attributed grammars. The given context-free grammar and the background knowledge allow one to restrict the space of relations and give a smaller representation of data. The basic idea of this method is that the learning problem of semantic functions is transformed to a propositional form and the hypothesis induced by a propositional learner is transformed back into semantic functions.
Categories and Subject Descriptors: D.3.2 [Programming Languages]: Language Classifications; I.2.6 [Artificial Intelligence]: Learning
Additional Key Words and Phrases: attribute grammar, machine learning, inductive logic programming, attribute-value learner
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