This study investigates the syntactic features of Slovene student writing by comparing essays from the Šolar 3.0 corpus (ages 13–19; primary and secondary school levels) with textbook texts from the Učbeniki 1.0 corpus aligned to the same educational stages. We apply quantitative syntactic analysis at two complementary levels: clause-type frequency (coordination, parataxis, and four types of subordination) and tree-based syntactic complexity measures (number of clauses, clauses per T-unit, and maximum parse-tree depth). Results show that students heavily rely on coordination and specific subordinate clauses (especially object and adverbial), producing more clauses per sentence and per T-unit than textbooks. However, their sentences tend to exhibit flatter syntactic structures, with shallower embedding in primary school and only modest increases in tree depth by secondary school. These findings reveal a divergence between surface-level complexity and hierarchical depth, highlighting developmental trends and instructional targets in written syntactic maturity. We conclude by discussing implications for syntactic development and directions for future research.

Large Language Models (LLMs) have demonstrated significant potential in natural language processing, but they depend on vast, diverse datasets, creating challenges for languages with limited resources. The paper presents a national initiative that addresses these challenges for Slovene. We outline strategies for large-scale text collection, including the creation of an online platform to engage the broader public in contributing texts and a communication campaign promoting openly accessible and transparently developed LLMs.

This paper introduces the upgrade of a training corpus for linguistic annotation of modern standard Slovene. The enhancement spans both the size of the corpus and the depth of annotation layers. The revised SUK 1.0 corpus, building on its predecessor ssj500k 2.3, has doubled in size, containing over a million tokens. This expansion integrates three preexisting open-access datasets, all of which have undergone automatic tagging and meticulous manual review across multiple annotation layers, each represented in varying proportions. These layers span tokenization, segmentation, lemmatization, MULTEXT-East morphology, Universal Dependencies, JOS-SYN syntax, semantic role labeling, named entity recognition, and the newly incorporated coreferences. The paper illustrates the annotation processes for each layer while also presenting the results of the new CLASSLA-Stanza annotation tool, trained on the SUK corpus data. As one of the fundamental language resources of modern Slovene, the SUK corpus calls for constant development, as outlined in the concluding section.

Annotation and analysis of corrections in learner corpora have always presented technical challenges, mainly on account of the fact that until now there has not been any standard tool available, and that original and corrected versions of texts have been mostly stored together rather than treated as individual texts. In this paper, we present CJVT Svala 1.0, the Slovene version of the SVALA tool, which was originally used for the annotation of Swedish learner language. The localisation into Slovene resulted in the development of several new features in SVALA such as the support for multiple annotation systems, localisation into other languages, and the support for more complex annotation systems. Adopting the parallel aligned approach to text visualisation and annotation, as well as storing the data, combined with the tool supporting this, i.e. SVALA, are proposed as new standards in Learner Corpus Research.

We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of this generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.

We describe a new version of the Gigafida reference corpus of Slovene. In addition to updating the corpus with new material and annotating it with better tools, the focus of the upgrade was also on its transformation from a general reference corpus, which contains all language variants including non-standard language, to the corpus of standard (written) Slovene. This decision could be implemented as new corpora dedicated specifically to non-standard language emerged recently. In the new version, the whole Gigafida corpus was deduplicated for the first time, which facilitates automatic extraction of data for the purposes of compilation of new lexicographic resources such as the collocations dictionary and the thesaurus of Slovene.