VerbAligNet: Unlocking Multilingual Exploration of Verbal Arguments

This paper presents a cross-lingual methodology for analyzing verbal argument structures to uncover shared syntax-semantic patterns among verbal complements across languages. The primary contribution is a novel semantic model for encoding verbal arguments in multiple languages. The methodology is rooted in the k-Multilingual Concept ( \(MC^k\) ) model, a state-of-the-art automated system designed for retrieving and aligning semantically-equivalent lexical items across k different languages. We integrated WordNet, BabelNet, and VerbNet into a framework that accommodates the unique demands of verbal context. The methodology is implemented in a highly-scalable pipeline, creating VerbAligNet, a new resource that encodes over 6k verbal arguments for 600+ verb senses, showcasing prevalent usage patterns across 9 valency frames on three languages. The evaluation demonstrates its accuracy in extracting semantically-equivalent verbal arguments for diverse verbs.