1. A method, in a data processing system having a processor and a memory storing instructions for implementing a natural language processing engine, a store of semantic types, and a store of units, conversions among units, and variants of unit names, for generating semantically equivalent variants of a natural language term, wherein the data processing system configured with the natural language processing engine comprises a question answering system, the method comprising: receiving, by the natural language processing engine, an input term for variant analysis, wherein the input term is an answer to a question forming a question and answer pair for an answer key of the question answering system; identifying, by the natural language processing engine executing on the data processing system, a semantic type of the input term based on the store of semantic types; extracting, by the natural language processing engine, a quantity and a unit from the input term based on the store of units, conversions among units, and variants of unit names; populating, by the natural language processing engine, type-specific templates at a level of specificity based on the input term based on the identified semantic type of the input term and the extracted quantity and unit to form a set of semantically equivalent variants of the input term; generating, by the natural language processing engine, a question and answer pair using the question and each semantically equivalent variant within the set of semantically equivalent variants of the input term to form a supplemented answer key; training, by the natural language processing engine, a question answering machine learning model for the question answering system using the supplemented answer key; and configuring the question answering system to generate in accordance with the question answering machine learning model.