Disentangling typical and atypical Russian acquisition patterns: an automated approach
DOI:
https://doi.org/10.36505/ExLing-2024/15/0002/000627Keywords:
Developmental Language Disorder, Machine learning, Narrative skills, Russian children, BilingualismAbstract
The present study investigated a machine learning approach for disentangling Developmental Language Disorder (DLD) from typical development in Russianspeaking children. We explored the BiSLI Corpora from CHILDES and conducted two studies: 1) with monolingual Russian children with typical development (TD) versus DLD; 2) with bilingual Russian-Dutch TD children and Russian monolingual children with DLD. The efficacy of using computational features was compared to that of traditional clinical measures for diagnosis of DLD. The results revealed that computational features, such as tf-idf vectors and n-gram models, performed at or above the clinical threshold for acceptable and good performance (using the “F1” measure) and demonstrated significantly better results than the traditional language sample analysis models.
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Copyright (c) 2024 Katsiaryna Aharodnik, Isabelle Barrière, Irina A. Sekerina, Valerie L. Shafer (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
