Words of any language are to some extent related thought the ways they are formed. For instance, the verb ‘exempl-ify’ and the noun ‘example-s’ are both based on the word ‘example’, but the verb is derived from it, while the noun is inflected. In Natural Language Processing of Russian, the inflection is satisfactorily processed; however, there are only a few machine-trackable resources that capture derivations even though Russian has both of these morphological processes very rich. Therefore, we devote this paper to improving one of the methods of constructing such resources and to the application of the method to a Russian lexicon, which results in the creation of the largest lexical resource of Russian derivational relations. The resulting database dubbed DeriNet.RU includes more than 300 thousand lexemes connected with more than 164 thousand binary derivational relations. To create such data, we combined the existing machine-learning methods that we improved to manage this goal. The whole approach is evaluated on our newly created data set of manual, parallel annotation. The resulting DeriNet.RU is freely available under an open license agreement.

Russian morphology has been studied for decades, but there is still no large high coverage resource that contains the derivational families (groups of words that share the same root) of Russian words. The number of words used in different areas of the language grows rapidly, thus the human-made dictionaries published long time ago cannot cover the neologisms and the domain-specific lexicons. To fill such resource gap, we have developed a rule-based framework for deriving words and we applied it to build a derivational morphology resource named DerivBase.Ru, which we introduce in this paper.