International Business Machines Corporation
COMPUTERIZED SELECTION OF SEMANTIC FRAME ELEMENTS FROM TEXTUAL TASK DESCRIPTIONS

Abstract:

A computer identifies, within a task description, words that correspond to semantic element labels for the task. The computer receives, from a task source operatively connected with the computer, a textual description of a task. The computer receives semantic element labels, element identification rules, and at least one reference sentence showing natural language semantic element label use. The computer parses the description to generate words for the semantic element label to generate, a Rule Match Values based on the element identification rules for the parsed words. The computer collects words having RMVs above a threshold into sets of associated of candidate words and generates, using a neural network trained on the reference sentence, Match Likelihood Values (MLVs) indicating whether the candidate words represent a semantic element label with which the candidate word is associated. The computer selects to represent the semantic element, the associated candidate word having a highest MLV.