@article{IR,
      recid = {8371},
      author = {McKinney-Bock, Katy and Li, Linying and Fergadiotis,  Gerasimos and Bedrick, Steven},
      title = {Improving machine learning models of paraphasia  classification using crowdsourced semantic features},
      publisher = {Oregon Health and Science University},
      address = {2020},
      number = {IR},
      abstract = {Confrontation naming tasks such as the Philadelphia Naming  Test (PNT) are essential tools for the assessment and  treatment of people with aphasia. One challenge to clinical  use of the PNT is the complexity of its scoring guidelines.  We build on work towards automatic classification of  paraphasias by (1) demonstrating that machine learning  models substantially improve performance on the paraphasia  classification task, (2) providing insight into the  behavior of the highest-performing model, and (3) reporting  results on experiments with contextual semantic and  item-level information. Our best model improves 8.1 F1  points over a manually-constructed decision tree  classifier. For development of the semantic features, we  used crowdsourcing methods to construct a dataset of  linguistic contexts from the visually presented items on  the PNT assessment.},
      url = {http://digitalcollections.ohsu.edu/record/8371},
      doi = {https://doi.org/10.6083/5d86p0976},
}