Latent Semantic Analysis Discriminates Children with Developmental Language Disorder (DLD) from Children with Typical Language Development

Computer based analyses offer a possibility for objective methods to assess semantic-linguistic quality of narratives at the text level. The aim of the present study is to investigate whether a semantic language impairment index (SELIMI) based on latent semantic analysis (LSA) can discriminate betwe...

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Bibliographic Details
Published in:Journal of psycholinguistic research 2019-06, Vol.48 (3), p.683-697
Main Authors: Bååth, Rasmus, Sikström, Sverker, Kalnak, Nelli, Hansson, Kristina, Sahlén, Birgitta
Format: Article
Language:English
Subjects:
Behavioral Science and Psychology
Child
Children
Cognitive Psychology
Comparative Analysis
Computational Linguistics
Developmental disabilities
Developmental language disorder
Female
General Language Studies and Linguistics
Humaniora och konst
Humanities
Humans
Identification
Insurance claims
Jämförande språkvetenskap och allmän lingvistik
Language Acquisition
Language Development
Language Development Disorders - diagnosis
Language disorders
Language Impairments
Languages and Literature
Latent semantic analysis
Machine learning
Male
Medicin och hälsovetenskap
Models, Statistical
Narration
Narratives
Oral Language
Psycholinguistics
Psycholinguistics - methods
Psychology
Semantic analysis
Semantic change
Semantic linguistic ability
Semantics
Språk och litteratur
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Summary:Computer based analyses offer a possibility for objective methods to assess semantic-linguistic quality of narratives at the text level. The aim of the present study is to investigate whether a semantic language impairment index (SELIMI) based on latent semantic analysis (LSA) can discriminate between children with developmental language disorder (DLD) and children with typical language development. Spoken narratives from 54 children with DLD and 54 age matched controls with typical language development were summarized in a semantic representation generated using LSA. A statistical model was trained to discriminate between children with DLD and children with typical language development, given the semantic vector representing each individual child’s narrative. The results show that SELIMI could distinguish between children with DLD and children with typical language development significantly better than chance and thus has a potential to complement traditional analyses focussed on form or on the word level.
ISSN:0090-6905
1573-6555
1573-6555
DOI:10.1007/s10936-018-09625-8