Atypical or impaired language is one of the core features of autism spectrum disorder (ASD). Yet, what the precise characteristics of language are in ASD and how they differ from those in other disorders such as Specific Language Impairment (SLI) is still substantially unknown. An important obstacle for the study of language in any disorder is that conventional structured instruments (i.e., instruments consisting of a sequence of items, each eliciting a - typically brief - response, such as the Clinical Evaluation of Language Fundamentals [CELF]) may not provide adequate breadth of information: Analysis of natural language samples is required The proposed research will build on recent progress in Natural Language Processing (NLP) technology, an area of Computer Science concerned with computational analysis of text. The goal of the proposed research is to develop and validate new NLP based methods that automatically measure language characteristics of ASD based on raw (i.e., not coded) transcripts of natural language samples. The objective is to improve the analysis of natural language samples by enhancing efficiency, reliability, and richness of information extracted. Data on three groups of children ages four to eight will be analyzed, obtained from an earlier study: ASD, SLI, and typically developing children. If successful, the new methods will have important impacts on research and clinical practice for ASD and for other disorders in which language is affected, by enabling analysis of more representative and ecologically valid natural language samples as well as by creating opportunities for discovery of currently unknown language characteristics of ASD by the effortless extraction of numerous language features.
Although atypical or impaired language is one of the core features of autism spectrum disorder (ASD), what the precise characteristics of language are in ASD and how they differ from those in other disorders such as Specific Language Impairment (SLI) is still substantially unknown, in part due to the paucity of instruments for the analysis of natural language samples. The goal of this project is to develop and apply Natural Language Processing technologies to automatically extract ASD-specific language characteristics from uncoded, 'raw' transcripts of natural language samples.
| MacFarlane, Heather; Gorman, Kyle; Ingham, Rosemary et al. (2017) Quantitative analysis of disfluency in children with autism spectrum disorder or language impairment. PLoS One 12:e0173936 |
| Gorman, Kyle; Olson, Lindsay; Hill, Alison Presmanes et al. (2016) Uh and um in children with autism spectrum disorders or language impairment. Autism Res 9:854-65 |
| Rouhizadeh, Masoud; Prud'hommeaux, Emily; van Santen, Jan et al. (2015) Measuring idiosyncratic interests in children with autism. Proc Conf Assoc Comput Linguist Meet 2015:212-217 |
| Rouhizadeh, Masoud; Sproat, Richard; van Santen, Jan (2015) Similarity Measures for Quantifying Restrictive and Repetitive Behavior in Conversations of Autistic Children. Proc Conf 2015:117-123 |
| Hill, Alison Presmanes; van Santen, Jan; Gorman, Kyle et al. (2015) Memory in language-impaired children with and without autism. J Neurodev Disord 7:19 |
| Gorman, Kyle; Bedrick, Steven; Kiss, Géza et al. (2015) Automated morphological analysis of clinical language samples. Proc Conf 2015:108-116 |
| Prud'hommeaux, Emily; Morley, Eric; Rouhizadeh, Masoud et al. (2014) COMPUTATIONAL ANALYSIS OF TRAJECTORIES OF LINGUISTIC DEVELOPMENT IN AUTISM. SLT Workshop Spok Lang Technol 2014:266-271 |
| Rouhizadeh, Masoud; Prud'hommeaux, Emily; Roark, Brian et al. (2013) Distributional semantic models for the evaluation of disordered language. Proc Conf 2013:709-714 |
| van Santen, Jan P H; Sproat, Richard W; Hill, Alison Presmanes (2013) Quantifying repetitive speech in autism spectrum disorders and language impairment. Autism Res 6:372-83 |
| Prud'hommeaux, Emily; Rouhizadeh, Masoud (2012) Automatic detection of pragmatic deficits in children with autism. Workshop Child Comput Interact 2012:1-6 |
