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This paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. A probabilistic model comprising Minimal Recursion Semantics (MRS) structures and lexicalist oriented semantic features is acquired. Lexical semantic roles enriching the MRS structures are inferred, which are useful to improve the accuracy of deep semantic parsing. Verb classes inference was also investigated, which, together with lexical semantic information provided by VerbNet and PropBank resources, can be substantially beneficial to the parse disambiguation task.

Mapping between compositional semantic representations and lexical semantic resources