Learning semantic functions of attribute grammars

Authors:
Tibor Gyimóthy;Tamás Horváth
Affiliations:
Hungarian Academy of Sciences, Research Group on Artificial Intelligence, Aradi vértanuk tere 1, H-6720 Szeged, Hungary;József Attila University, Department of Applied Informatics, Árpád tér 2, H-6720 Szeged, Hungary
Venue:
Nordic Journal of Computing
Year:
1997

Citing 10
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Abstract

Attribute 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.