2021, Hoppe, Fabian, Dessì, Danilo, Sack, Harald, Alam, Mehwish, Buscaldi, Davide, Cochez, Michael, Osborne, Francesco, Reforgiato Recupero, Diego, Sack, Harald

Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.