ParAlg: A Paraphasia Algorithm for Multinomial Classification of Picture Naming Errors.

Purpose: A preliminary version of a paraphasia classification algorithm (henceforth called ParAlg) has previously been shown to be a viable method for coding picture naming errors. The purpose of this study is to present an updated version of ParAlg, which uses multinomial classification, and comprehensively evaluate its performance when using two different forms of transcribed input.

Method: A subset of 11,999 archival responses produced on the Philadelphia Naming Test were classified into six cardinal paraphasia types using ParAlg under two transcription configurations: (a) using phonemic transcriptions for responses exclusively () and (b) using phonemic transcriptions for nonlexical responses and orthographic transcriptions for lexical responses ().

Standardizing Assessment of Spoken Discourse in Aphasia: A Working Group With Deliverables.

Purpose The heterogeneous nature of measures, methods, and analyses reported in the aphasia spoken discourse literature precludes comparison of outcomes across studies (e.g., meta-analyses) and inhibits replication.

Classification of Semantic Paraphasias: Optimization of a Word Embedding Model.

In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects () is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance.