While the literal usage metric captures the distributional semantic information of the context, we find that some other linguistic cues are also significant for usage detection (such as whether the subject of the sentence is a person); therefore, we allow our model to further refine through unsupervised methods.
we treat the usage (figurative or literal) as a hidden variable in probabilistic latent variable models, and we define a set of features that are linguistically relevant for idiom usage detection as observables.