@article{586,
  author = {Libby Barak and Sammy Floyd and Adele Goldberg},
  title = {Modeling the acquisition of words with multiple meanings},
  abstract = {<p>Learning vocabulary is essential to successful communication. Complicating this task is the under-appreciated fact that most common words are associated with multiple senses (are polysemous ) (e.g., baseball capvs. capof a bottle), while other words are homonymous, evoking meanings that are unrelated to one another (e.g., baseball batvs. flying bat ). Models of human word learning have thus far failed to represent this level of naturalistic complexity. We extend a feature-based computational model to allow for multiple meanings, while capturing the gradient distinction between polysemy and homonymy by using structured sets of features. Results confirm that the present model correlates better with human data on novel word learning tasks than the existing feature-based model.</p>},
  year = {2019},
  journal = {Proceedings of the Society for Computation in Linguistics},
  volume = {2},
  number = {1},
  pages = {216{\textendash}225},
}