Abstract: Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement an improved search query generation system, the method comprising inputting a natural language question; parsing the natural language question into a parse tree; identifying argument positions comprising one or more argument position terms; for each argument position: comparing a head term's discriminator score against a threshold discriminator score; and if the head term surpasses the threshold discriminator score, adding the head term as a required term to an improved search query; and outputting the improved search query.

Abstract: A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.

Abstract: A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.

Abstract: Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement an improved search query generation system, the method comprising inputting a natural language question; parsing the natural language question into a parse tree; identifying argument positions comprising one or more argument position terms; for each argument position: comparing a head term's discriminator score against a threshold discriminator score; and if the head term surpasses the threshold discriminator score, adding the head term as a required term to an improved search query; and outputting the improved search query.

Abstract: A mechanism is provided in a data processing system having a processor and a memory storing a store of semantic types and instructions for implementing a natural language processing engine for generating semantically equivalent variants of a natural language term. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on a store of semantic types. The natural language processing engine performs a type-specific series of rule-based expansions of the input term based on the identified semantic type of the input term to form a set of semantically equivalent variants of the input term. The natural language processing engine performs a natural language processing operation using the input term and the set of semantically equivalent variants of the input term.

Abstract: A mechanism is provided 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. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on the store of semantic types. The natural language processing engine extracts a quantity and a unit from the input term based on the store of units, conversions among units, and variants of unit names. The natural language processing engine populates 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.

Abstract: A mechanism is provided in a data processing system having a processor and a memory storing a store of semantic types and instructions for implementing a natural language processing engine for generating semantically equivalent variants of a natural language term. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on a store of semantic types. The natural language processing engine performs a type-specific series of rule-based expansions of the input term based on the identified semantic type of the input term to form a set of semantically equivalent variants of the input term. The natural language processing engine performs a natural language processing operation using the input term and the set of semantically equivalent variants of the input term.

Abstract: A mechanism is provided 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. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on the store of semantic types. The natural language processing engine extracts a quantity and a unit from the input term based on the store of units, conversions among units, and variants of unit names. The natural language processing engine populates 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.

Abstract: A mechanism is provided in a data processing system having a processor and a memory storing a store of semantic types and instructions for implementing a natural language processing engine for generating a question/answer pair list with semantically equivalent variants. The mechanism generates a user interface for generating a question/answer pair list. The mechanism receives 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. The input term comprises the answer term. The natural language processing engine executing on the data processing system identifies a semantic type of the answer term based on the store of semantic types. The natural language processing engine performs 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.