8. A system for establishing is-a relationships, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors and storing executable and operational data effective to cause the one or more processors to: receive a query from a user, the query including a string and one or more context words; generate a first co-occurrence vector indicating co-occurrence statistics for terms in a reference corpus with respect to the string; generate a second co-occurrence vector indicating terms and co-occurrence statistics for terms in current social media postings; determine a difference between the first and second co-occurrence vectors; determine that the difference between the first and second co-occurrence vectors exceeds a threshold condition; in response to determining that the difference between the first and second co-occurrence vectors exceeds a threshold condition, request from the user additional context words; retrieve homonym concepts corresponding to the string and concept vectors for each homonym concept; compare the concept vectors to the one or more context words; select at least one homonym concept according to the comparison; retrieve from a category-concept mapping database at least one category for the selected at least one homonym concept, the category-concept mapping database establishing is-a relationships between a plurality of categories and a plurality of concepts; and transmit the retrieved at least one category for display to the user.
8. A system for establishing is-a relationships, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors and storing executable and operational data effective to cause the one or more processors to: receive a query from a user, the query including a string and one or more context words; generate a first co-occurrence vector indicating co-occurrence statistics for terms in a reference corpus with respect to the string; generate a second co-occurrence vector indicating terms and co-occurrence statistics for terms in current social media postings; determine a difference between the first and second co-occurrence vectors; determine that the difference between the first and second co-occurrence vectors exceeds a threshold condition; in response to determining that the difference between the first and second co-occurrence vectors exceeds a threshold condition, request from the user additional context words; retrieve homonym concepts corresponding to the string and concept vectors for each homonym concept; compare the concept vectors to the one or more context words; select at least one homonym concept according to the comparison; retrieve from a category-concept mapping database at least one category for the selected at least one homonym concept, the category-concept mapping database establishing is-a relationships between a plurality of categories and a plurality of concepts; and transmit the retrieved at least one category for display to the user. 10. The system of claim 8 , wherein the executable and operational data are further effective to cause the one or more processors to generate the concept vectors of the homonym concepts by, for each homonym concept: determining a first word usage for an article in a reference corpus corresponding to the homonym concept; determining a second word usage for documents using the homonym concept in current social media documents; and combining the first and second word usages to generate the concept vector for the homonym concept.