Paraphrasing verbal metonymy through computational methods
This addresses the understudied issue of verbal metonymy in computational linguistics, with potential applications in academia and technology, but it is incremental as it builds on existing distributional semantics methods.
The paper tackled the problem of paraphrasing verbal metonymy using computational methods, finding that a Skip-gram model outperformed Continuous bag of words with better-than-chance accuracy and a strong positive relationship (phi coefficient = 0.61) between model classification and human judgment.
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.