Some houghts on Corpus and General Linguistics

В статье рассматриваются основные подходы и их влияние на общее развитие лингвистических знаний в рамках корпусной лингвистики.The article is devoted to a discussion of dominant approaches developed within the framework ofCorpus Linguistics (CL) and their influence on the general theory of language

Corpora in Text-Based Russian Studies

This chapter focuses on textual data that are collected for a specific purpose, which are usually referred to as corpora. Scholars use corpora when they examine existing instances of a certain phenomenon or to conduct systematic quantitative analyses of occurrences, which in turn reflect habits, attitudes, opinions, or trends. For these contexts, it is extremely useful to combine different approaches. For example, a linguist might analyze the frequency of a certain buzzword, whereas a scholar in the political, cultural, or sociological sciences might attempt to explain the change in language usage from the data in question.Peer reviewe

Integration of computer-aided language learning into formal university-level L2 instruction

This paper presents our experience from pilot studies оn integration of intelligent learning and tutoring tools into official curricula for foreign/second-language (L2) learning. We report specifically on initial studies with learners of Russian as a second language at major universities in Italy and in Finland. An important challenge in both of these educational situations is the heterogeneous nature of the student contingent, including the presence of a sizable proportion of ‘heritage’ learners. Furthermore, the groups are often very large, which motivates the integration of an ICALL system. We describe the first integration attempt, an analysis of the emerging aspects and problems, and the design of a new experiment, which is on-going and takes into account the lessons learned. To the best of our knowledge, this is the first report on large-scale ICALL studies involving substantial numbers of ‘high-stakes’ learners of Russian at the intermediate-to-advanced levels – i.e., learners beyond the elementary level

Evaluation tracks on plagiarism detection algorithms for the Russian language

The paper presents a methodology and preliminary results for evaluating plagiarism detection algorithms for the Russian language. We describe the goals and tasks of the PlagEvalRus workshop, dataset creation, evaluation setup, metrics, and results

Evaluation tracks on plagiarism detection algorithms for the Russian language

The paper presents a methodology and preliminary results for evaluating plagiarism detection algorithms for the Russian language. We describe the goals and tasks of the PlagEvalRus workshop, dataset creation, evaluation setup, metrics, and results