5. A method of recognizing named entities in a query with a recognition module operating on a processor, the method comprising: defining a seed set comprising a seed named entity; assigning a classification to the seed named entity based on a predefined taxonomy; training a probabilistic topic model based on the seed named entity and the classification; receiving an input query from a user; detecting, by the processor, another named entity in the input query; representing the input query as one or more triples (e, t, c), wherein e represents the detected other named entity, t represents a context of the detected other named entity, and c represents a particular classification for the detected other named entity that is determined based at least in part on the trained probabilistic topic model and the predefined taxonomy; calculating a joint probability for individual ones of the one or more triples; identifying a largest joint probability associated with a respective one of the one or more triples; predicting, by the recognition module, an assigned classification for the detected other named entity based at least in part on the identified largest joint probability; and outputting the detected other named entity and the assigned classification to the user.
5. A method of recognizing named entities in a query with a recognition module operating on a processor, the method comprising: defining a seed set comprising a seed named entity; assigning a classification to the seed named entity based on a predefined taxonomy; training a probabilistic topic model based on the seed named entity and the classification; receiving an input query from a user; detecting, by the processor, another named entity in the input query; representing the input query as one or more triples (e, t, c), wherein e represents the detected other named entity, t represents a context of the detected other named entity, and c represents a particular classification for the detected other named entity that is determined based at least in part on the trained probabilistic topic model and the predefined taxonomy; calculating a joint probability for individual ones of the one or more triples; identifying a largest joint probability associated with a respective one of the one or more triples; predicting, by the recognition module, an assigned classification for the detected other named entity based at least in part on the identified largest joint probability; and outputting the detected other named entity and the assigned classification to the user. 7. The method of claim 5 , wherein the seed set is selected based at least in part on a data set comprising a plurality of search queries.