1. A method for generating a language processing model, comprising: obtaining one or more multi-media labels, wherein each multi-media label is based on a corresponding multi-media item that is associated with a content item, wherein each particular multi-media label is associated with one or more n-grams from the content item that is associated with the multi-media item that corresponds to the particular multi-media label, and wherein an n-gram is a digital representation of one or more words or groups of characters; including, in a language corpus, the one or more n-grams associated with each of the one or more multi-media labels; 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 one or more of the 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 one or more multi-media labels, wherein the multi-media context probabilities are based on the associations between the one or more multi-media labels and the one or more n-grams.
1. A method for generating a language processing model, comprising: obtaining one or more multi-media labels, wherein each multi-media label is based on a corresponding multi-media item that is associated with a content item, wherein each particular multi-media label is associated with one or more n-grams from the content item that is associated with the multi-media item that corresponds to the particular multi-media label, and wherein an n-gram is a digital representation of one or more words or groups of characters; including, in a language corpus, the one or more n-grams associated with each of the one or more multi-media labels; 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 one or more of the 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 one or more multi-media labels, wherein the multi-media context probabilities are based on the associations between the one or more multi-media labels and the one or more n-grams. 6. The method of claim 1 , wherein the association, of at least an individual multi-media label that is of the one or more multi-media labels and that corresponds to a multi-media item associated with an individual content item, is based on one or more of: characteristics of an author of the individual content item, characteristics of user interactions with the individual content item, characteristics of users who have interacted with the individual content item, or any combination thereof.