1. A method for generating a language processing model, comprising: obtaining multiple content items, wherein each content item is associated with one or more multi-media items and comprises one or more n-grams, wherein an n-gram is a digital representation of one or more words or groups of characters; for each selected content item of the multiple content items: identifying one or more multi-media labels for the one or more multi-media items associated with the selected content item; assigning to the identified one or more multi-media labels at least some of the one or more n-grams of the selected content item; and including, in a language corpus, the one or more n-grams of the selected content item; and generating the language processing model comprising a probability distribution by computing, for each selected n-gram of multiple n-grams in the language corpus, a frequency that the selected n-gram occurs in the language corpus, wherein the probability distribution can take representations of multi-media labels as parameters, and wherein at least some probabilities provided by the probability distribution are multi-media context probabilities indicating a probability of a chosen n-gram occurring, given that the chosen n-gram is associated with provided multi-media labels, wherein the multi-media context probabilities are based on the assigning of the identified one or more multi-media labels.