Heuristically Informed Unsupervised Idiom Usage Recognition
Changsheng Liu, Rebecca Hwa · 2018
Depending on the surrounding context, an idiomatic expression may be interpreted figuratively or literally.This paper proposes an unsupervised learning method for recognizing the intended usages of idioms.We treat the possible usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space.Crucially, we show that distributional semantics serves as a helpful heuristic for formulating a literal usage metric to estimate the likelihood that the idiom is intended literally.This information can then guide the unsupervised training process for the probabilistic models.Experiments show that our overall model performs competitively against supervised methods.