title: Exploring a corpus-based approach for detecting language impairment in monolingual English-speaking children.
abstract: OBJECTIVE
  This paper explores the use of an automated method for analyzing narratives of monolingual English speaking children to accurately predict the presence or absence of a language impairment. The goal is to exploit corpus-based approaches inspired by the fields of natural language processing and machine learning.
  
  
  METHODS
  We extract a large variety of features from language samples and use them to train language models and well known machine learning algorithms as the underlying predictors. The methods are evaluated on two different datasets and three language tasks. One dataset contains samples of two spontaneous narrative tasks performed by 118 children with an average age of 13 years and a second dataset contains play sessions from over 600 younger children with an average age of 6 years.
  
  
  RESULTS
  We compare results against a cut off baseline method and show that our results are far superior, reaching F-measures of over 85% in two of the three language tasks, and 48% in the third one.
  
  
  CONCLUSIONS
  The different experiments we present here show that corpus based approaches can yield good prediction results in the problem of language impairment detection. These findings warrant further exploration of natural language processing techniques in the field of communication disorders. Moreover, the proposed framework can be easily adapted to analyze samples in languages other than English since most of the features are language independent or can be customized with little effort.
authors.affiliation: Department of Computer Science, The University of Texas at Dallas, Richardson, USA.
authors.forename: Keyur
authors.initials: K
authors.lastname: Gabani
authors.forename: Thamar
authors.initials: T
authors.lastname: Solorio
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
authors.forename: Khairun-Nisa
authors.initials: KN
authors.lastname: Hassanali
authors.forename: Christine A
authors.initials: CA
authors.lastname: Dollaghan
mesh_terms: D000293:Adolescent; D000367:Age Factors; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D002663:Child Language; D005260:Female; D006801:Humans; D007360:Intelligence; D007802:Language; D007804:Language Development; D007806:Language Disorders / Q000175:diagnosis* / Q000523:psychology; D007807:Language Tests; D008297:Male; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D010988:Play and Playthings