8. A computer program product comprising: a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises a natural language processing engine, wherein the natural language processing engine, when executed on a data processing system, causes the data processing system to: generate a user interface for generating a question/answer pair list; receive user input in the user interface specifying a question and an answer term and specifying an answer type from a list of previously created answer types, wherein the input term comprises the answer term; identify, by the natural language processing engine executing on the data processing system, a semantic type of the answer term based on the store of semantic types; perform, by the natural language processing engine, a type-specific series of rule-based expansions of the answer term based on the identified semantic type of the answer term of the answer term: add, by the natural language processing engine, at least one semantically equivalent variant from the set of semantically equivalent variants of the answer term in association with the specified question to the question/answer pair list to form an expanded question/answer pair list; and train, by the natural language processing engine, a question answering machine learning model for a question answering cognitive system using the expanded question/answer pair list as ground truth.
8. A computer program product comprising: a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises a natural language processing engine, wherein the natural language processing engine, when executed on a data processing system, causes the data processing system to: generate a user interface for generating a question/answer pair list; receive user input in the user interface specifying a question and an answer term and specifying an answer type from a list of previously created answer types, wherein the input term comprises the answer term; identify, by the natural language processing engine executing on the data processing system, a semantic type of the answer term based on the store of semantic types; perform, by the natural language processing engine, a type-specific series of rule-based expansions of the answer term based on the identified semantic type of the answer term of the answer term: add, by the natural language processing engine, at least one semantically equivalent variant from the set of semantically equivalent variants of the answer term in association with the specified question to the question/answer pair list to form an expanded question/answer pair list; and train, by the natural language processing engine, a question answering machine learning model for a question answering cognitive system using the expanded question/answer pair list as ground truth. 12. The computer program product of claim 8 , wherein performing a type-specific series of rule-based expansions comprises generating number variants based on the extracted quantity.