Automating Intended Target Identification for Paraphasias in Discourse Using a Large Language Model.

PURPOSE: To date, there are no automated tools for the identification and fine-grained classification of paraphasias within discourse, the production of which is the hallmark characteristic of most people with aphasia (PWA). In this work, we fine-tune a large language model (LLM) to automatically predict paraphasia targets in Cinderella story retellings. METHOD: Data consisted of 332 Cinderella story retellings containing 2,489 paraphasias from PWA, for which research assistants identified their intended targets. We supplemented these training data with 256 sessions from control participants, to which we added 2,415 synthetic paraphasias. We conducted four experiments using different training data configurations to fine-tune the LLM to automatically "fill in the blank" of the paraphasia with a predicted target, given the context of the rest of the story retelling. We tested the experiments' predictions against our human-identified targets and stratified our results by ambiguity of the targets and clinical factors. RESULTS: The model trained on controls and PWA achieved 50.7% accuracy at exactly matching the human-identified target. Fine-tuning on PWA data, with or without controls, led to comparable performance. The model performed better on targets with less human ambiguity and on paraphasias from participants with fluent or less severe aphasia. CONCLUSIONS: We were able to automatically identify the intended target of paraphasias in discourse using just the surrounding language about half of the time. These findings take us a step closer to automatic aphasic discourse analysis. In future work, we will incorporate phonological information from the paraphasia to further improve predictive utility. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24463543.

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 (phonemic-only) and (b) using phonemic transcriptions for nonlexical responses and orthographic transcriptions for lexical responses (orthographic-lexical). Agreement was quantified by comparing ParAlg-generated paraphasia codes between configurations and relative to human-annotated codes using four metrics (positive predictive value, sensitivity, specificity, and F1 score). An item-level qualitative analysis of misclassifications under the best performing configuration was also completed to identify the source and nature of coding discrepancies. RESULTS: Agreement between ParAlg-generated and human-annotated codes was high, although the orthographic-lexical configuration outperformed phonemic-only (weighted-average F1 scores of .78 and .87, respectively). A qualitative analysis of the orthographic-lexical configuration revealed a mix of human- and ParAlg-related misclassifications, the former of which were related primarily to phonological similarity judgments whereas the latter were due to semantic similarity assignment. CONCLUSIONS: ParAlg is an accurate and efficient alternative to manual scoring of paraphasias, particularly when lexical responses are orthographically transcribed. With further development, it has the potential to be a useful software application for anomia assessment. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22087763.