Deverbal nouns pose challenges for semantic annotation frameworks that aim to represent event structures consistently across lexical categories. This paper examines problematic phenomena in the annotation of deverbal nouns in Czech and Latin within the Universal Meaning Representation (UMR) framework, addressing both manual graph construction and rule-based automatic conversion from existing resources. Current UMR guidelines lack operational criteria for deciding when a noun should be treated as an eventive concept, particularly in the absence of a PropBank-like lexicon with sufficient nominal coverage. We therefore propose practical annotation principles: deverbal nouns denoting events (such as učení ‘teaching’), results of events (řešení ‘solution’), or event participants (učitel ‘teacher’) should be related to underlying event concepts (represented as verbs in their particular senses, i.e., učit-001 ‘to teach’, vyřešit-001 ‘to solve’, and učit-001 ‘to teach’, respectively), while other deverbal nouns should remain unrelated to respective events (such as učebna ‘teaching room’). To reduce inter-annotator variation, we further suggest systematic strategies for selecting verbal labels, including the use of light-verb constructions, synonymous verbs, and a preference for imperfective verbs in Czech aspectual pairs. For automatic conversion, we outline a rule-based approach that combines multiple lexical resources and frequency-based heuristics to identify corresponding verb senses. Our findings provide guidelines for more consistent UMR annotation across languages.

The paper presents the first shared task on parsing Uniform Meaning Representation (UMR), a graph-based framework for cross-linguistic semantic annotation of typologically diverse languages. The task requires systems to enrich plain text with sentence-level structure, node–token alignment, and document-level relations. It involves processing data for seven languages from four language families (Indo-European, Sino-Tibetan, Na-Dene, and Algic). Six languages have at least some training data; for one language, data is not available, leading to a zero-shot scenario. The training dataset as well as the gold-standard test set for all seven languages is released and made available for follow-up research. We present the task setup and evaluation methodology, using two graph matching approaches – a traditional, and an alignment-sensitive one, tailored specifically for UMR. Two participating systems are compared, each representing different modeling approaches. Results highlight the challenges of UMR parsing, particularly for alignment prediction and document-level semantics, and reveal substantial variation across languages and annotation conditions.

Uniform Meaning Representation (UMR) is a semantic framework designed to represent the meaning of texts in a structured and interpretable manner. In this paper, we evaluate the results of the automatic conversion of existing resources to UMR, focusing on Czech (PDT-C treebank) and Latin (LDT treebank). We present both quantitative and qualitative evaluations based on a comparison between manually and automatically generated UMR structures for a sample of Czech and Latin sentences. The findings indicate comparable results of the automatic conversion for both languages. The key challenges prove to be the higher level of semantic abstraction required by UMR and the fact that UMR allows for capturing semantic structure in multiple ways, potentially with varying levels of granularity.