Russian Learner Corpus (RLC) is a large collection of learner texts in Russian written by native speakers of over forty languages. Learner errors in part of the corpus are manually corrected and annotated. Diverging from conventional error classifications, which typically focus on isolated lexical and grammatical features, the RLC error classification intends to highlight learners’ strategies employed in the process of text production, such as derivational patterns and syntactic relations (including agreement and government). In this paper, we present two open datasets derived from RLC: a manually annotated full-text dataset and a dataset with crowdsourced corrections for individual sentences. In addition, we introduce an automatic error annotation tool that, given an original sentence and its correction, locates and labels errors according to a simplified version of the RLC error-type system. We evaluate the performance of the tool on manually annotated data from RLC.

This paper discusses the Routinicon, a new constructicographic resource for the description of conversational routines. Conversational routines are defined as conventional formulaic expressions that language speakers use in standard extralinguistic situations (cf. Bless you! as a reaction to sneezing or Who’s there? as a typical answer to a knock on the door). The Routinicon’s goal is to accumulate the routines that constitute the inventory of conventional expressions in Russian language and systematically describe them in a way that would enable future cross-linguistic comparison and typological research. Conceptually, the Routinicon is a natural extension of such projects as the Russian Constructicon and Pragmaticon. It inherits their approach to the systematization of phraseological units as well as to the data collection. At the same time, the new project focuses on a fundamentally different domain of units and hence offers a radically new structure of linguistic annotation. Its principles and challenges are addressed in the paper.