Derivational relations are an important element in defining meanings, as they help to explore word-formation schemes and predict senses of derivates (derived words).In this work, we analyse different methods of representing derivational forms obtained from WordNetfrom quantitative vectors to contextual learned embedding methods -and compare ways of classifying the derivational relations occurring between them.Our research focuses on the explainability of the obtained representations and results.The data source for our research is plWordNet, which is the wordnet of the Polish language and includes a rich set of derivation examples.

In the paper, we present the process of adding morphological information to the Polish WordNet (plWordNet).We describe the reasons for this connection and the intuitions behind it.We also draw attention to the specificity of the Polish morphology.We show in which tasks the morphological information is important and how the methods can be developed by extending them to include combined morphological information based on WordNet.