@InProceedings{zayed-mccrae-buitelaar:2018:W18-09,
  author    = {Zayed, Omnia  and  McCrae, John Philip  and  Buitelaar, Paul},
  title     = {Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning},
  booktitle = {Proceedings of the Workshop on Figurative Language Processing},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {81--90},
  abstract  = {Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.},
  url       = {http://www.aclweb.org/anthology/W18-0910}
}