The paper presents the issue of collocability and collocations in Russian and gives a survey of a wide range of dictionaries both printed and online ones that describe collocations. Our project deals with building a database that will include dictionary and statistical collocations. The former can be described in various lexicographic resources whereas the latter can be extracted automatically from corpora. Dictionaries differ among themselves, the information is given in various ways, making it hard for language learners and researchers to acquire data. A number of dictionaries were analyzed and processed to retrieve verified collocations, however the overlap between the lists of collocations extracted from them is still rather small. This fact indicates there is a need to create a unified resource which takes into account collocability and more examples. The proposed resource will also be useful for linguists and for studying Russian as a foreign language. The obtained results can be important for machine learning and for other NLP tasks, for instance, automatic clustering of word combinations and disambiguation.

Without any doubt corpora are vital tools for linguistic studies and solution for applied tasks. Although corpora opportunities are very useful, there is a need of another kind of software for further improvement of linguistic research as it is impossible to process huge amount of linguistic data manually. The Sketch Engine representing itself a corpus tool which takes as input a corpus of any language and corresponding grammar patterns. The paper describes the writing of Sketch grammar for the Russian language as a part of the Sketch Engine system. The system gives information about a word’s collocability on concrete dependency models, and generates lists of the most frequent phrases for a given word based on appropriate models. The paper deals with two different approaches to writing rules for the grammar, based on morphological information, and also with applying word sketches to the Russian language. The data evidences that such results may find an extensive use in various fields of linguistics, such as dictionary compiling, language learning and teaching, translation (including machine translation), phraseology, information retrieval etc.