This paper presents the development of a corpus of annotated multiword expressions (MWEs) for Ukrainian. The resource covers four major categories of MWEs: verbal, nominal, adjectival/adverbial, and functional. We describe the methodology used for data selection, the annotation scheme, and the procedures employed during annotation. In addition, the paper discusses some specific types of MWE constructions, illustrating their usage with numerous examples and addressing complex and borderline cases. The resulting corpus is an important resource for linguistic studies and NLP tasks involving MWEs, and is publicly accessible https://gitlab.com/parseme/sharedtask-data/-/tree/master/2.0?ref_type=heads.

We describe a Ukrainian-Russian code-switching corpus of Ukrainian Parliamentary Session Transcripts. The corpus includes speeches entirely in Ukrainian, Russian, or various types of mixed speech and allows us to see how speakers switch between these languages depending on the communicative situation. The paper describes the process of creating this corpus from the official multilingual transcripts using automatic language detecting and publicly available metadata on the speakers. On this basis, we consider possible reasons for the change in the number of Ukrainian speakers in the parliament and present the most common patterns of bilingual Ukrainian and Russian code-switching in parliamentarians’ speeches.

This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2021 Conference. Ten teams participated in the competition. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task. Detailed valuation information is available on the shared task web page.

Today’s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people’s lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent annotators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. These texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity.