1. A computer-implemented method of generating a model for predicting whether a content word is being used figuratively, comprising: accessing, with a processing system, a plurality of training texts, each training text including content words, and each content word having a corresponding annotation indicating whether the content word is being used figuratively in the associated training text; accessing, with the processing system, a plurality of topic models generated from a first corpus; for each content word in the plurality of training texts, assigning, with the processing system, a plurality of topic-model feature scores to the content word, each topic-model feature score being associated with one of the plurality of topic models and determined based on: determining, with the processing system, a first probability of the content word being used in a topic represented by the topic model; determining, with the processing system, a second probability of the content word being used in a second corpus; and computing, with the processing system, the topic-model feature score using the first probability and the second probability; and generating, with the processing system, a prediction model for predicting whether a content word is being used figuratively, the generating being based on at least one of the plurality of topic-model feature scores and the annotations.
12. The method of claim 1 , comprising: for each sentence in a corpus: comparing, with the processing system, the sentence to one or more predetermined patterns for identifying similes; if the sentence matches any of the one or more predetermined patterns, identifying, with the processing system, at least one simile source word in the sentence and at least one contextual content word in the sentence other than the simile source word; and creating, in a data source, a record representing an observance of the at least one simile source word and the at least one contextual content word occurring in the same sentence; for each content word in the plurality of training texts: identifying, with the processing system, a sentence in which the content word occurs; identifying, with the processing system, at least one other content word in the sentence; determining, with the processing system, at least one frequency value based on using the data source, each frequency value representing a frequency of the content word and one of the at least one other content word occurring in a same sentence where the content word is being used as a simile source word; and determining, with the processing system, a non-literal-context feature score based on the at least one frequency value; wherein the generating of the prediction model is further based on the non-literal-context feature scores.