Recent progress within the UniDive COST Action on the compilation of universal guidelines for the annotation of non-verbal multiword expressions (MWEs) has provided an opportunity to improve and expand the work previously done within the PARSEME COST Action on the annotation of verbal multiword expressions in the SUK 1.0 Training Corpus of Slovene. A segment of the training corpus had already been annotated with verbal MWEs during PARSEME. As a follow-up and part of the New Grammar of Modern Standard Slovene (NSSSS) project, the same segment was annotated with non verbal MWEs, resulting in approximately 6, 500 sentences annotated by at least three annotators (described in Gantar et al., 2019). Since then, the entire SUK 1.0 was also manually annotated with UD part-of-speech tags. In the paper, we present an analysis of the MWE annotations exported from the corpus along with their part-of-speech structures through the lens of Universal Dependencies. We discuss the usefulness of the data in terms of potential insight for the further compilation and fine-tuning of guidelines particularly for non-verbal MWEs, and conclude with our plans for future work.

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.

We present version 1.3 of the PARSEME multilingual corpus annotated with verbal multiword expressions. Since the previous version, new languages have joined the undertaking of creating such a resource, some of the already existing corpora have been enriched with new annotated texts, while others have been enhanced in various ways. The PARSEME multilingual corpus represents 26 languages now. All monolingual corpora therein use Universal Dependencies v.2 tagset. They are (re-)split observing the PARSEME v.1.2 standard, which puts impact on unseen VMWEs. With the current iteration, the corpus release process has been detached from shared tasks; instead, a process for continuous improvement and systematic releases has been introduced.

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.

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year’s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed.