1. A method, in an information handling system comprising a processor and a memory, for analyzing concept vectors over time to detect changes in a corpus, the method comprising: generating, by the system, at least a first concept vector set V 1 , . . . , V′k+b derived from a concatenation of a first set of concept sequences and a second set of concept sequences over k concepts that are shared by the first and second sets of concept sequences and b concepts that are only in the second set of concept sequences and applied to the vector learning component, where the second set of concept sequences is effectively collected after collection of the first set of concept sequences; generating, by the system, at least a second concept vector set VL 1 , . . . , VLh derived from a third set of concept sequences identified in the concatenation of the first and second sets of concept sequences as being central to a specified technology area T over h concepts that are extracted from the corpus and applied to a vector learning component; and performing, by the system, a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect one or more disruptive concepts in the second set of concept sequences by analyzing relationship strengths between concepts to identify market trends for answering questions submitted to the information handling system, wherein analyzing relationship strengths comprises (a) computing cosine distances between each of the first vector set V′k+1, . . . , V′k+b and each of the second vector set VL 1 , . . . , VLh, and (b) sorting vectors V′k+1, . . . , V′k+b to identify one or more disruptive concepts based on the computed cosine distances.
1. A method, in an information handling system comprising a processor and a memory, for analyzing concept vectors over time to detect changes in a corpus, the method comprising: generating, by the system, at least a first concept vector set V 1 , . . . , V′k+b derived from a concatenation of a first set of concept sequences and a second set of concept sequences over k concepts that are shared by the first and second sets of concept sequences and b concepts that are only in the second set of concept sequences and applied to the vector learning component, where the second set of concept sequences is effectively collected after collection of the first set of concept sequences; generating, by the system, at least a second concept vector set VL 1 , . . . , VLh derived from a third set of concept sequences identified in the concatenation of the first and second sets of concept sequences as being central to a specified technology area T over h concepts that are extracted from the corpus and applied to a vector learning component; and performing, by the system, a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect one or more disruptive concepts in the second set of concept sequences by analyzing relationship strengths between concepts to identify market trends for answering questions submitted to the information handling system, wherein analyzing relationship strengths comprises (a) computing cosine distances between each of the first vector set V′k+1, . . . , V′k+b and each of the second vector set VL 1 , . . . , VLh, and (b) sorting vectors V′k+1, . . . , V′k+b to identify one or more disruptive concepts based on the computed cosine distances. 9. The method of claim 1 , wherein performing the NLP analysis comprises detecting an appearance of one or more disruptive concepts that have newly appeared with strong relationships to concepts in the third set of concept sequences that are central to the specified technology area T.