Relative clauses are grammatical constructions that are of relevance in both typical and impaired language development. Thus, the accurate identification of these structures in child language samples is clinically important. In recent years, computer software has been used to assist in the automated analysis of clinical language samples. However, this software has had only limited success when attempting to identify relative clauses. The present study explores the development and clinical importance of relative clauses and investigates the accuracy of the software used for automated identification of these structures. Two separate collections of language samples were used. The first collection included 10 children with language impairment, ranging in age from 7;6 to 11;1 (years;months), 10 age-matched peers, and 10 language-matched peers. A second collection contained 30 children considered to have typical speech and language skills and who ranged in age from 2;6 to 7;11. Language samples were manually coded for the presence of relative clauses (including those containing a relative pronoun, those without a relative pronoun and reduced relative clauses). These samples were then tagged using computer software and finally tabulated and compared for accuracy. ANACOVA revealed a significant difference in the frequency of relative clauses containing a relative pronoun but not for those without a relative pronoun nor for reduce relative clauses. None of the structures were significantly correlated with age; however, frequencies of both relative clauses with and without relative pronouns were correlated with mean length of utterance. Kappa levels revealed that agreement between manual and automated coding was relatively high for each relative clause type and highest for relative clauses containing relative pronouns.

Adverbial clauses are grammatical constructions that are of relevance in both typical language development and impaired language development. In recent years, computer software has been used to assist in the automated analysis of clinical language samples. This software has attempted to accurately identify adverbial clauses with limited success. The present study investigated the accuracy of software for the automated identification of adverbial clauses. Two separate collections of language samples were used. One collection included 10 children with language impairment, with ages ranging from 7;6 to 11;1 (years;months), 10 age-matched peers,and 10 language-matched peers. A second collection contained 30 children ranging from 2;6 to 7;11 in age, with none considered to have language or speech impairments. Language sample utterances were manually coded for the presence of adverbial clauses (both finite and non-finite). Samples were then automatically tagged using the computer software. Results were tabulated and compared for accuracy. ANOVA revealed differences in frequencies of so-adverbial clauses whereas ANACOVA revealed differences in frequencies of both types of finite adverbial clauses. None of the structures were significantly correlated with age; however, frequencies of both types of finite adverbial clauses were correlated with mean length of utterance. Kappa levels revealed that agreement between manual and automated coding was high on both types of finite adverbial clauses.

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.