The semantic annotation of clinical narratives is particularly challenging due to the complexity of medical discourse and the need to integrate linguistic, semantic, and domain-specific information within a unified framework. Existing schemes tend to fall into two categories: general-purpose frameworks, which offer robust linguistic modelling but lack specialised medical representation, and domain-specific schemes, which capture clinical content yet often fail to distinguish fundamental semantic types, especially eventive expressions and referential entities. To address this gap, this study proposes Med2Story Referential, a new extension of the Text2Story annotation scheme (Silvano et al., 2021; Leal et al., 2022) (based on ISO 24617-9: 2019 ) dedicated to referential entities in clinical narratives. Building on previous work that introduced a specialised branch for eventive entities (Fernandes et al., 2025a), and informed by the UMLS Metathesaurus and expert validation from a consultant haematologist, the extension introduces eight referential categories that refine the representation of clinical actors, substances, biological entities, instruments, and documentation. The results show that ISO 24617-9: 2019 can be applied to this type of text; however, several adaptations are required, particularly with regard to the grammatical domain and the inclusion of specialised domain labels. Nonetheless, the annotation experiment conducted to validate our proposal showed that the annotation scheme and its accompanying guidelines enable a comprehensive and detailed representation of both grammatical and medical aspects. Moreover, the results indicate that the scheme can be applied effectively by annotators without medical expertise.

We introduce SemEval-2025 Task 10 on Multilingual Characterization and Extraction of Narratives from Online News, which focuses on the identification and analysis of narratives in online news media. The task is structured into three subtasks: (1) Entity Framing, to identify the roles that relevant entities play within narratives, (2) Narrative Classification, to assign documents fine-grained narratives according to a given, topic-specific taxonomy of narrative labels, and (3) Narrative Extraction, to provide a justification for the dominant narrative of the document. To this end, we analyze news articles across two critical domains, Ukraine-Russia War and Climate Change, in five languages: Bulgarian, English, Hindi, Portuguese, and Russian. This task introduces a novel multilingual and multifaceted framework for studying how online news media construct and disseminate manipulative narratives. By addressing these challenges, our work contributes to the broader effort of detecting, understanding, and mitigating the spread of propaganda and disinformation. The task attracted a lot of interest: 310 teams registered, with 66 submitting official results on the test set.

The development of a robust annotation scheme and corresponding guidelines is crucial for producing annotated datasets that advance both linguistic and computational research. This paper presents a case study that outlines a methodology for designing an annotation scheme and its guidelines, specifically aimed at representing morphosyntactic and semantic information regarding temporal features, as well as medical information in medical reports written in Portuguese. We detail a multi-step process that includes reviewing existing frameworks, conducting an annotation experiment to determine the optimal approach, and designing a model based on these findings. We validated the approach through a pilot experiment where we assessed the reliability and applicability of the annotation scheme and guidelines. In this experiment, two annotators independently annotated a patient’s medical report consisting of six documents using the proposed model, while a curator established the ground truth. The analysis of inter-annotator agreement and the annotation results enabled the identification of sources of human variation and provided insights for further refinement of the annotation scheme and guidelines.

The definition of rigorous and well-structured annotation schemes is a key element in the advancement of Natural Language Processing (NLP). This paper aims to compare the performance of a general-purpose annotation scheme — Text2Story, based on the ISO 24617-1 standard — with that of a domain-specific scheme — i2b2 — in the context of clinical narrative annotation; and to assess the feasibility of harmonizing ISO 24617-1, originally designed for general-domain applications, with a specialized extension tailored to the medical domain. Based on the results of this comparative analysis, we present Med2Story, a medical-specific extension of ISO 24617-1 developed to address the particularities of clinical text annotation.

We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.

We present polyNarrative, a new multilingual dataset of news articles, annotated for narratives. Narratives are overt or implicit claims, recurring across articles and languages, promoting a specific interpretation or viewpoint on an ongoing topic, often propagating mis/disinformation. We developed two-level taxonomies with coarse- and fine-grained narrative labels for two domains: (i) climate change and (ii) the military conflict between Ukraine and Russia. We collected news articles in four languages (Bulgarian, English, Portuguese, and Russian) related to the two domains and manually annotated them at the paragraph level. We make the dataset publicly available, along with experimental results of several strong baselines that assign narrative labels to news articles at the paragraph or the document level. We believe that this dataset will foster research in narrative detection and enable new research directions towards more multi-domain and highly granular narrative related tasks.