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Abstract
This work explores a new robust approach for Semantic Parsing of unrestricted texts. Our approach considers Semantic Parsing as a Consistent Labelling Problem CLP, allowing the integration of several knowledge types syntactic and semantic obtained from different sources linguistic and statistic. The current implementation obtains 95\% accuracy in model identification and 72\% in case-role filling.
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