Speakers
Description
This paper evaluates the results of using GPT-4o mini language model batch processing with image recognition capability to align 1,572 images of 398 polysemous nouns in the Dictionary of the Slovenian Standard Language (second edition) to their specific dictionary senses, and it compares them to the results of the manual image-to-sense alignment process. The images were manually assigned to entries in a previous task, but no sense information was provided at the time. The language model showed relatively high overall agreement with the human annotator (i.e., 85.1%). In cases in which multiple senses were selected per image in both manual and automated annotation, the agreement was even slightly higher (i.e. in 89.4% of all sense evaluations). The agreement rate was higher when the language model evaluated only the matching senses and lower when it also evaluated the non-matching senses within the entry.
