Information Extraction (IE), encompassing Named Entity Recognition (NER), Named Entity Linking (NEL), and Relation Extraction (RE), is critical for transforming the rapidly growing volume of scientific publications into structured, actionable knowledge. This need is especially evident in fast-evolving biomedical fields such as the gut-brain axis, where research investigates complex interactions between the gut microbiota and brain-related disorders. Existing biomedical IE benchmarks, however, are often narrow in scope and rely heavily on distantly supervised or automatically generated annotations, limiting their utility for advancing robust IE methods. We introduce GutBrainIE, a benchmark based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. While grounded in the gut-brain axis, the benchmark’s rich schema, multiple tasks, and combination of highly curated and weakly supervised data make it broadly applicable to the development and evaluation of biomedical IE systems across domains.

This paper presents current results of a work-in-progress project on the aims, goals, and methods for compiling a state-of-the-art morphosyntactically annotated corpus of Corfioto, the endangered Balkan Venetan variety of the Corfiot Jews. It gives an outline of the workflow for building, archiving, managing and annotating the first mixed-language corpus of original oral and written data of the Corfiot Jews, based on the Universal Dependencies (UD) framework and introduces the design and the implementation of an application for the Interactive MorPhosyntactic Annotation of Corfioto (IMPACT). The creation and the annotation of the corpus serves three goals: i) attain a quantitative analysis of variation in available data for the analysis of contact-induced syntactic change in clausal complementation in Corfioto; ii) enable the creation of a gold standard and the training of a model for the linguistic annotation of all data in the Universal Dependencies framework; and iii) contribute to the ever-growing research in the development of language resources and tools for endangered and low-resource contact varieties via the collaboration of computational, theoretical and fieldwork linguists.