Name
Abstract | ||
|---|---|---|
The phenotypic complexity of Autism Spectrum Disorder motivates the application of modern computational methods to large collections of observational data, both for improved clinical diagnosis and for better scientific understanding. We have begun to create a corpus of annotated language samples relevant to this research, and we plan to join with other researchers in pooling and publishing such resources on a large scale. The goal of this paper is to present some initial explorations to illustrate the opportunities that such datasets will afford. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.18653/v1/w16-0308 | CLPsych@HLT-NAACL |
Field | DocType | Volume |
Autism,Cognitive science,Computer science,Artificial intelligence,Natural language processing | Conference | 2016 |
ISSN | Citations | PageRank |
0736-587X | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julia Parish-Morris | 1 | 0 | 0.34 |
| Mark Liberman | 2 | 126 | 30.65 |
| Neville Ryant | 3 | 1 | 0.70 |
| Christopher Cieri | 4 | 123 | 42.44 |
| Leila Bateman | 5 | 0 | 1.01 |
| Emily Ferguson | 6 | 0 | 1.01 |
| Robert T. Schultz | 7 | 299 | 31.91 |