Learning to map text to graph-based meaning representations via grammar induction
Smaranda Muresan · 2008
We argue in favor of using a graph-based representation for language meaning and propose a novel learning method to map natural language text to its graph-based meaning representation. We present a grammar formalism, which combines syntax and semantics, and has ontology constraints at the rule level. These constraints establish links between language expressions and the entities they refer to in the real world. We present a relational learning algorithm that learns these grammars from a small representative set of annotated examples, and show how this grammar induction framework and the ontology-based semantic representation allow us to directly map text to graph-based meaning representations.