18. A machine-readable tangible and non-transitory medium having information for identifying candidate queries related to a trending topic, wherein the information, when read by the machine, causes the machine to perform the following: receiving a user query; matching contents of the received user query to one or more trending topics stored within a database, the trending topics being identified based on an analysis of content of one or more real-time content sources by receiving documents from the one or more real-time content sources, segmenting the received documents into words and named entities, organizing the received documents into two or more portions of documents each defined by a time interval within a historical time period, determining a volume for each of a predetermined number of topics, and for each of the predetermined number of topics: calculating a mean volume by taking an average of the determined volumes for every portion within the historical time period, calculating a deviation between a determined volume associated with a portion defined by a most recent time interval of the historical time period to the mean volume, and identifying one of the predetermined number of topics as a trending topic when the topic is associated with a determined volume that has a calculated deviation from the mean volume that exceeds a threshold value; retrieving one or more named entities stored in association with the matched one or more trending topics; and returning the retrieved one or more named entities.
18. A machine-readable tangible and non-transitory medium having information for identifying candidate queries related to a trending topic, wherein the information, when read by the machine, causes the machine to perform the following: receiving a user query; matching contents of the received user query to one or more trending topics stored within a database, the trending topics being identified based on an analysis of content of one or more real-time content sources by receiving documents from the one or more real-time content sources, segmenting the received documents into words and named entities, organizing the received documents into two or more portions of documents each defined by a time interval within a historical time period, determining a volume for each of a predetermined number of topics, and for each of the predetermined number of topics: calculating a mean volume by taking an average of the determined volumes for every portion within the historical time period, calculating a deviation between a determined volume associated with a portion defined by a most recent time interval of the historical time period to the mean volume, and identifying one of the predetermined number of topics as a trending topic when the topic is associated with a determined volume that has a calculated deviation from the mean volume that exceeds a threshold value; retrieving one or more named entities stored in association with the matched one or more trending topics; and returning the retrieved one or more named entities. 19. The medium of claim 18 , wherein matching contents of the received user query to one or more trending topics stored within a database comprises: segmenting the received user query into words and named entities; calculating a sum of probabilities assigned to the words and named entities of the received user query and stored in association with the one or more trending topics within the database to determine relevance values for the one or more trending topics with respect to the words and named entities of the received user query; comparing each of the relevance values to a threshold value; and selecting trending topics associated with relevance values that satisfy the threshold value.