This paper presents the Croatian verb lexicon Verbion that describes verbs on multiple levels. The semantic level includes verb senses, corresponding semantic classes according to VerbNet and WordNet, as well as semantic frames based on FrameNet. Each verb sense is linked to one or more valency frames, which include corpus-based examples accompanied by syntactic, morphological, and semantic analyses of each argument. This study focuses on assigning FrameNet frames to the verb misliti ‘think’ and its prefixed forms. Based on 170 manually annotated sentences, the paper discusses the advantages and challenges of assigning semantic frames to Croatian verbs.

Understanding the relation between the meanings of words is an important part of comprehending natural language. Prior work has either focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs), with some exceptions. Given the rarity of highly multilingual benchmarks, it is unclear to what extent PLMs capture relational knowledge and are able to transfer it across languages. To start addressing this question, we propose MultiLexBATS, a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages, such as Bambara, Lithuanian, and Albanian. As experiment on cross-lingual transfer of relational knowledge, we test the PLMs’ ability to (1) capture analogies across languages, and (2) predict translation targets. We find considerable differences across relation types and languages with a clear preference for hypernymy and antonymy as well as romance languages.