18. A system for generating a model, comprising: one or more processors; a memory; an interface configured to obtain multiple content items, wherein each of the multiple content items 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; a multi-media labeler configured to, for each selected content item of the multiple content items: identify one or more multi-media labels for the one or more multi-media items associated with the selected content item; assign 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 include, in a language corpus, the one or more n-grams of the selected content item; and a language processing model trainer configured to generate the model by computing a probability distribution that indicates, 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.
18. A system for generating a model, comprising: one or more processors; a memory; an interface configured to obtain multiple content items, wherein each of the multiple content items 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; a multi-media labeler configured to, for each selected content item of the multiple content items: identify one or more multi-media labels for the one or more multi-media items associated with the selected content item; assign 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 include, in a language corpus, the one or more n-grams of the selected content item; and a language processing model trainer configured to generate the model by computing a probability distribution that indicates, 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. 19. The system of claim 18 , wherein the multi-media labeler is configured to identify the one or more multi-media labels for the one or more multi-media items associated with the selected content item by identifying, in at least one of the one or more multi-media items, at least one of: an object, a place, a person, or any combination thereof; and wherein the one or more multi-media labels are selected from a set of pre-defined labels.