In this paper we focus on verbal multiword expressions (VMWEs) in Bulgarian and Romanian as reflected in the wordnets of the two languages. The annotation of VMWEs relies on the classification defined within the PARSEME Cost Action. After outlining the properties of various types of VMWEs, a cross-language comparison is drawn, aimed to highlight the similarities and the differences between Bulgarian and Romanian with respect to the lexicalization and distribution of VMWEs. The contribution of this work is in outlining essential features of the description and classification of VMWEs and the cross-language comparison at the lexical level, which is essential for the understanding of the need for uniform annotation guidelines and a viable procedure for validation of the annotation.

This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.