An exploration of automated narrative analysis via machine learning
Autoři:
Sharad Jones aff001; Carly Fox aff002; Sandra Gillam aff003; Ronald B. Gillam aff003
Působiště autorů:
Department of Mathematics and Statistics, Utah State University, Logan, Utah, United States of America
aff001; Department of Special Education and Rehabilitation, Utah State University, Logan, Utah, United States of America
aff002; Department of Communication Disorders and Deaf Education, Utah State University, Logan, Utah, United States of America
aff003
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224634
Souhrn
The accuracy of four machine learning methods in predicting narrative macrostructure scores was compared to scores obtained by human raters utilizing a criterion-referenced progress monitoring rubric. The machine learning methods that were explored covered methods that utilized hand-engineered features, as well as those that learn directly from the raw text. The predictive models were trained on a corpus of 414 narratives from a normative sample of school-aged children (5;0-9;11) who were given a standardized measure of narrative proficiency. Performance was measured using Quadratic Weighted Kappa, a metric of inter-rater reliability. The results indicated that one model, BERT, not only achieved significantly higher scoring accuracy than the other methods, but was consistent with scores obtained by human raters using a valid and reliable rubric. The findings from this study suggest that a machine learning method, specifically, BERT, shows promise as a way to automate the scoring of narrative macrostructure for potential use in clinical practice.
Klíčová slova:
Language – Machine learning – Machine learning algorithms – Microstructure – Neural networks – Semantics – Undergraduates
Zdroje
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