  Verbal metonymy has received relatively scarce attention in the field of
computational linguistics despite the fact that a model to accurately
paraphrase metonymy has applications both in academia and the technology
sector. The method described in this paper makes use of data from the British
National Corpus in order to create word vectors, find instances of verbal
metonymy and generate potential paraphrases. Two different ways of creating
word vectors are evaluated in this study: Continuous bag of words and
Skip-grams. Skip-grams are found to outperform the Continuous bag of words
approach. Furthermore, the Skip-gram model is found to operate with
better-than-chance accuracy and there is a strong positive relationship (phi
coefficient = 0.61) between the model's classification and human judgement of
the ranked paraphrases. This study lends credence to the viability of modelling
verbal metonymy through computational methods based on distributional
semantics.
