We investigate the potential of using ChatGPT to annotate complex linguistic phenomena, such as language of evaluation, attitude and emotion. For this, we automatically annotate 11 texts in English, which represent spoken popular science, and evaluate the annotations manually. Our results show that ChatGPT has good precision in itemisation, i.e. detecting linguistic items in the text that carry evaluative meaning. However, we also find that the recall is very low. Besides that, we state that the tool fails in labeling the detected items with the correct categories on a more fine-grained level of granularity. We analyse the errors to find systematic errors related to specific categories in the annotation scheme.

This study sets out to investigate the feasibility of using ChatGPT to translate citizen-oriented administrative texts into German Easy Language, a simplified, rule-based language variety that is adapted to the needs of people with reading impairments. We use ChatGPT to translate selected texts from websites of German public authorities using two strategies, i.e. linguistic and holistic. We analyse the quality of the generated texts based on different criteria, such as correctness, readability, and syntactic complexity. The results indicated that the generated texts are easier than the standard texts, but that they still do not fully meet the established Easy Language standards. Additionally, the content is not always rendered correctly.