Automated morphological analysis of clinical language samples

Abstract

Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.

Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences

“…LSA provides clinicians with tangible goals for therapy unlikely to emerge from results of standardized testing but that can be prioritized for intervention (Overton and Wren 2014). In the absence of norm-referenced assessments for children speaking non-mainstream dialects or English as a Second Language, LSA also can provide less biased and more informative information about a child's expressive language skills and needs (Caesar and Kohler 2007;Gorman 2010).…”

“…However, there are a number of practical issues in using LSA for clinical purposes that tend to diminish the frequency (and depth) of its use in actual clinical practice (Gorman 2010). While the self-reported use of LSA has been steadily climbing in reports from 1993 to 2000 (Hux 1993;Eisenberg, Fersko, and Lundgren 2001;Kemp and Klee 1997), most SLPs (Speech-Language Pathologists) report compiling relatively short samples in real-time notation and using them primarily to compute mean length of utterance (MLU; Price, Hendricks, and Cook 2010;Finestack and Satterlund 2018), despite the fact that MLU is not a good standalone measure for identifying language impairment (Eisenberg, Fersko, and Lundgren 2001).…”

“…Some studies have estimated that it takes up to 8 hours of training and from 45 minutes to one hour of work after a transcript has been generated to compute DSS (Long and Channell 2001;Cochran and Masterson 1995). One study (Gorman 2010) estimated that it takes more than 30 minutes per sample following transcription to compute the Index of Productive Syntax (IPSYN; Scarborough 1990). Hand computation of most LSA measures, even the time-honored MLU, is quite prone to error.…”

Your Laptop to the Rescue: Using the Child Language Data Exchange System Archive and CLAN Utilities to Improve Child Language Sample Analysis

“…3 In the absence of norm-referenced assessments for children speaking nonmainstream dialects or English as a second language, LSA also can provide less biased and more informative information about the child's expressive language skills and needs. 4,5 However, there are several practical issues in using LSA for clinical purposes that tend to diminish the frequency (and depth) of its use in actual clinical practice. 5 Although the self-reported use of LSA has been steadily climbing in reports from 1993 to 2000, [6][7][8] most speech-language pathologists (SLPs) report compiling relatively short samples in real-time notation and using them primarily to compute mean length of utterance (MLU), 2 despite the fact that it is not a good stand-alone measure for identifying language impairment and does not deeply inform the child's grammatical development, let alone proficiency with vocabulary or other aspects of expressive language.…”

“…4,5 However, there are several practical issues in using LSA for clinical purposes that tend to diminish the frequency (and depth) of its use in actual clinical practice. 5 Although the self-reported use of LSA has been steadily climbing in reports from 1993 to 2000, [6][7][8] most speech-language pathologists (SLPs) report compiling relatively short samples in real-time notation and using them primarily to compute mean length of utterance (MLU), 2 despite the fact that it is not a good stand-alone measure for identifying language impairment and does not deeply inform the child's grammatical development, let alone proficiency with vocabulary or other aspects of expressive language. 8 Fewer than one-third of respondents in one study computed any additional measures, the most popular being Developmental Sentence Score (DSS).…”