Mapping between compositional semantic representations and lexical semantic resources: towards accurate deep semantic parsing

Authors:
Sergio Roa;Valia Kordoni;Yi Zhang
Affiliations:
Saarland University, Germany and University of Freiburg, Germany;Saarland University, Germany;Saarland University, Germany
Venue:
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Year:
2008

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Abstract

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.