Inductive learning of lexical semantics with typed unification grammars
نویسندگان
چکیده
In the last decade machine learning techniques based on logic such as Inductive Logic Programming (ILP) have started being used in learning grammars from corpora. While the first approaches were based on the translation of grammar into first-order predicate logic, an attempt has been made recently to adapt the ILP learning schema to the feature constraint logic of typed-unification grammars. In this framework, the learner applies in turn generalisation and specialisation to the typed feature structures representing the grammar in order to improve its coverage of the training corpus provided. In this paper we demonstrate how the lexical resource WordNet could be incorporated into an existing grammar learning tool and show how lexical semantic constraints could be learned on the basis of the ontology information.
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A Framework for Inductive Learning of Typed-Unification Grammars
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