@article{IR,
      author = {Salem, Alexandra C. and Bedrick, Steven and Gale, Robert},
      url = {http://digitalcollections.ohsu.edu/record/9252},
      title = {Fine-tuning BERT for the semantic classifier of the PNT},
      publisher = {Oregon Health and Science University},
      abstract = {Aphasia is a language disorder resulting from a stroke or  brain injury. Individuals with aphasia make paraphasias:  speech errors consisting of an unintended word. This work  focuses on automatically classifying semantic similarity of  the paraphasia. We train a modern language model called  BERT alongside a binary classifier to categorize each  transcribed response to a PNT item as semantically similar  to the target or not. We evaluate the accuracy of this  classification compared with clinician scores. We also  compare the results of this model to a previously used  language model called word2vec.},
      number = {IR},
      doi = {https://doi.org/10.6083/rx913q54v},
      recid = {9252},
      address = {2021},
}