Predictive similarity scoring subsystem in a natural language understanding (nlu) framework

Abstract

Present embodiments include an agent automation framework having a similarity scoring subsystem that performs meaning representation similarity scoring to facilitate extraction of artifacts to address an utterance. The similarity scoring subsystem identifies a CCG form of an utterance-based meaning representation and queries a database to retrieve a comparison function list that enables quantifications of similarities between the meaning representation and candidates within a search space. The comparison functions enable the similarity scoring subsystem to perform computationally-cheapest and/or most efficient comparisons before other comparisons. The similarity scoring subsystem may determine an initial similarity score between the particular meaning representation and the candidates of the search space, then prune non-similar candidates from the search space. Selective search space pruning enables the similarity scoring subsystem to iteratively compare more data of the meaning representation to the search space via increasingly-complex comparison functions, while narrowing the search space to potentially-matching candidates.