SkeletonScore: Guiding a Semantic Parser to Better Results by Example

The task of semantic parsing is to map natural-language sentences to logical forms representing the underlying meanings of those sentences. Typically, resolving semantic ambiguity is considered to be a side effect of semantic parsing. However a large number of errors in parsing can be attributed to incorrect sense disambiguation in the first place. This can arise from the selection of an incorrect semantic role or semantic type by the parser. This paper applies a knowledge-based algorithm to guide a semantic parser to simultaneously select better semantic types and roles. The algorithm takes into account semantic roles and ontology types to reduce restriction violations arising from incorrect semantic role or type choices, hence increasing the total accuracy of the semantic parser.