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.
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. 9. The method of claim 1 , comprising: for each content word that is being used as an adjective or a verb in the plurality of training texts: if the content word is an adjective, identifying, with the processing system, an associated word, the associated word being a noun modified by the adjective; if the content word is a verb, identifying, with the processing system, an associated word, the associated word being a direct object of the verb; determining, with the processing system, a first concreteness rating for the content word and a second concreteness rating for the associated word; determining, with the processing system, a concreteness difference feature score based on the first concreteness rating and the second concreteness rating; wherein the generating of the prediction model is further based on the concreteness difference feature scores.