@InProceedings{rosen:2018:W18-09,
  author    = {Rosen, Zachary},
  title     = {Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues},
  booktitle = {Proceedings of the Workshop on Figurative Language Processing},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {102--109},
  abstract  = {The current study seeks to implement a deep learning classification algorithm us- ing argument-structure level representation of metaphoric constructions, for the identifica- tion of source domain mappings in metaphoric utterances. It thus builds on previous work in computational metaphor interpretation (Mohler et al. 2014; Shutova 2010; Bolle-gala \& Shutova 2013; Hong 2016; Su et al. 2017) while implementing a theoretical frame- work based off of work in the interface of metaphor and construction grammar (Sullivan 2006, 2007, 2013). The results indicate that it is possible to achieve an accuracy of approx- imately 80.4% using the proposed method, combining construction grammatical features with a simple deep learning NN. I attribute this increase in accuracy to the use of con- structional cues, extracted from the raw text of metaphoric instances.},
  url       = {http://www.aclweb.org/anthology/W18-0912}
}