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

Research output: Contribution to journalArticle


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.


External organisations
  • Karolinska University Hospital
  • Karolinska Institutet
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics


  • Developmental language disorder, Latent semantic analysis, Narratives, Semantic linguistic ability
Original languageEnglish
Pages (from-to)683-697
JournalJournal of Psycholinguistic Research
Issue number3
Early online date2019 Jan 25
Publication statusPublished - 2019
Publication categoryResearch