  This paper presents a corpus manually annotated with named entities for six
Slavic languages - Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian.
This work is the result of a series of shared tasks, conducted in 2017-2023 as
a part of the Workshops on Slavic Natural Language Processing. The corpus
consists of 5 017 documents on seven topics. The documents are annotated with
five classes of named entities. Each entity is described by a category, a
lemma, and a unique cross-lingual identifier. We provide two train-tune dataset
splits - single topic out and cross topics. For each split, we set benchmarks
using a transformer-based neural network architecture with the pre-trained
multilingual models - XLM-RoBERTa-large for named entity mention recognition
and categorization, and mT5-large for named entity lemmatization and linking.
