  The goal of visual word sense disambiguation is to find the image that best
matches the provided description of the word's meaning. It is a challenging
problem, requiring approaches that combine language and image understanding. In
this paper, we present our submission to SemEval 2023 visual word sense
disambiguation shared task. The proposed system integrates multimodal
embeddings, learning to rank methods, and knowledge-based approaches. We build
a classifier based on the CLIP model, whose results are enriched with
additional information retrieved from Wikipedia and lexical databases. Our
solution was ranked third in the multilingual task and won in the Persian
track, one of the three language subtasks.
