Automated Grammatical Tagging of Language Samples from Children with and without Language Impairment
Grammatical classification ("tagging") of words in language samples is a component of syntactic analysis for both clinical and research purposes. Previous studies have shown that probability-based software can be used to tag samples from adults and typically-developing children with high (...
| Main Author: | |
|---|---|
| Format: | Others |
| Published: |
BYU ScholarsArchive
2003
|
| Subjects: | |
| Online Access: | https://scholarsarchive.byu.edu/etd/1139 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2138&context=etd |
| Summary: | Grammatical classification ("tagging") of words in language samples is a component of syntactic analysis for both clinical and research purposes. Previous studies have shown that probability-based software can be used to tag samples from adults and typically-developing children with high (about 95%) accuracy. The present study found that similar accuracy can be obtained in tagging samples from school-aged children with and without language impairment if the software uses tri-gram rather than bi-gram probabilities and large corpora are used to obtain probability information to train the tagging software. |
|---|