We present a new corpus with coreference annotation, Russian Coreference Corpus (RuCoCo). The goal of RuCoCo is to obtain a large number of annotated texts while maintaining high inter-annotator agreement. RuCoCo contains news texts in Russian, part of which were annotated from scratch, and for the rest the machine-generated annotations were refined by human annotators. The size of our corpus is one million words and around 150,000 mentions. We make the corpus publicly available.

The paper is devoted to the creation of the semantic sketches for English verbs. The pilot corpus consists of the English-Russian sketch pairs and is aimed to show what kind of contrastive studies the sketches help to conduct. Special attention is paid to the cross-language differences between the sketches with similar semantics. Moreover, we discuss the process of building a semantic sketch, and analyse the mistakes that could give insight to the linguistic nature of sketches.

This paper presents the results of the study devoted to the applicability of SOTA methods for morphological corpus annotation (based on GramEval2020) for analytical sociolinguistic research. The study shows that statistically successful technologies of morphosyntactic annotation for such purposes create a number of problems for researchers if they are used purely i.e. without any linguistic knowledge. In this paper, methods for improving the morphological annotation, successfully implemented in GICR, from the point of view of its reliability are presented.