Automation of the Northwestern Narrative Language Analysis System.

Abstract

Purpose Analysis of spontaneous speech samples is important for determining patterns of language production in people with aphasia. To accomplish this, researchers and clinicians can use either hand coding or computer-automated methods. In a comparison of the two methods using the hand-coding NNLA (Northwestern Narrative Language Analysis) and automatic transcript analysis by CLAN (Computerized Language Analysis), Hsu and Thompson (2018) found good agreement for 32 of 51 linguistic variables. The comparison showed little difference between the two methods for coding most general (i.e., utterance length, rate of speech production), lexical, and morphological measures. However, the NNLA system coded grammatical measures (i.e., sentence and verb argument structure) that CLAN did not. Because of the importance of quantifying these aspects of language, the current study sought to implement a new, single, composite CLAN command for the full set of 51 NNLA codes and to evaluate its reliability for coding aphasic language samples. Method Eighteen manually coded NNLA transcripts from eight people with aphasia and 10 controls were converted into CHAT (Codes for the Human Analysis of Talk) files for compatibility with CLAN commands. Rules from the NNLA manual were translated into programmed rules for CLAN computation of lexical, morphological, utterance-level, sentence-level, and verb argument structure measures. Results The new C-NNLA (CLAN command to compute the full set of NNLA measures) program automatically computes 50 of the 51 NNLA measures and generates the results in a summary spreadsheet. The only measure it does not compute is the number of verb particles. Statistical tests revealed no significant difference between C-NNLA results and those generated by manual coding for 44 of the 50 measures. C-NNLA results were not comparable to manual coding for the six verb argument measures. Conclusion Clinicians and researchers can use the automatic C-NNLA to analyze important variables required for quantification of grammatical deficits in aphasia in a way that is fast, replicable, and accessible without extensive linguistic knowledge and training.