1. A method establishing is-a relationships, the method comprising: receiving, by a server, a query from a user, the query including a string and one or more context words; generating a first co-occurrence vector indicating co-occurrence statistics for terms in a reference corpus with respect to the string; generating a second co-occurrence vector indicating terms and co-occurrence statistics for terms in current social media postings; determining a difference between the first and second co-occurrence vectors; determining 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, requesting from the user additional context words; retrieving homonym concepts corresponding to the string and concept vectors for each homonym concept; comparing the concept vectors to the one or more context words; selecting at least one homonym concept according to the comparison; retrieving 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 transmitting, by the server, the retrieved at least one category for display to the user.
1. A method establishing is-a relationships, the method comprising: receiving, by a server, a query from a user, the query including a string and one or more context words; generating a first co-occurrence vector indicating co-occurrence statistics for terms in a reference corpus with respect to the string; generating a second co-occurrence vector indicating terms and co-occurrence statistics for terms in current social media postings; determining a difference between the first and second co-occurrence vectors; determining 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, requesting from the user additional context words; retrieving homonym concepts corresponding to the string and concept vectors for each homonym concept; comparing the concept vectors to the one or more context words; selecting at least one homonym concept according to the comparison; retrieving 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 transmitting, by the server, the retrieved at least one category for display to the user. 3. The method of claim 1 , further comprising generating the concept vectors for 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.