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The goal of visual word sense disambiguation is to find the image that bestmatches the provided description of the word's meaning. It is a challengingproblem, requiring approaches that combine language and image understanding. Inthis paper, we present our submission to SemEval 2023 visual word sensedisambiguation shared task. The proposed system integrates multimodalembeddings, learning to rank methods, and knowledge-based approaches. We builda classifier based on the CLIP model, whose results are enriched withadditional information retrieved from Wikipedia and lexical databases. Oursolution was ranked third in the multilingual task and won in the Persiantrack, one of the three language subtasks.

OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation