Research in Citation Context Analysis (CCA) has produced numerous taxonomic schemes that vary from three to 12+ categories, with different granularities and no mappings between frameworks, severely limiting systematic comparison and progress. Despite decades of study, CCA methods have largely relied on fragmented frameworks that treat citation tasks independently, ignoring systematic relationships between function classification, sentiment analysis, and importance assessment. To address these research gaps, we present three integrated contributions. First, we develop UniCite, a two-level taxonomy (six primary functions, 12 subcategories, two orthogonal dimensions) that systematically integrates three existing schemes. Second, we develop a comprehensive dataset of 4,017 citations combining established resources with 1,547 newly extracted citations from 2018-2024 publications, all manually annotated under our unified framework. Third, we demonstrate systematic task relationships through multi-task learning, achieving 21.1% relative improvement in subfunction classification over single-task approaches.

This paper describes the participation of the RCLN team at the Visual Word Sense Disambiguation task at SemEval 2023. The participation was focused on the use of CLIP as a base model for the matching between text and images with additional information coming from captions generated from images and the generation of images from the prompt text using Stable Diffusion. The results we obtained are not particularly good, but interestingly enough, we were able to improve over the CLIP baseline in Italian by recurring simply to the generated images.