In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022. We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to classify memes as either misogynistic or not. This model uses a majority voting ensemble structure built on traditional classifiers and additional image information such as age, gender and nudity estimations. The second model uses a RoBERTa classifier on the text transcriptions to additionally identify the type of problematic ideas the memes perpetuate. Our submissions perform above all organizer submitted baselines. For binary misogyny classification, our system achieved the fifth place on the leaderboard, with a macro F1-score of 0.665. For multi-label classification identifying the type of misogyny, our model achieved place 19 on the leaderboard, with a weighted F1-score of 0.637.

The CLARIN Concept Registry (CCR) is the common semantic ground for most CMDI-based profiles to describe language-related resources in the CLARIN universe. While the CCR supports semantic interoperability within this universe, it does not extend beyond it. The flexibility of CMDI, however, allows users to use other term or concept registries when defining their metadata components. In this paper, we describe our use of schema.org, a light ontology used by many parties across disciplines.