Constructing a Lexical Resource of Russian Derivational Morphology

Ch. 298. P. 2788–2797.

Kyjánek L., Lyashevskaya O., Nedoluzhko A., Vodolazsky D., Žabokrtský Z.

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.

Keywords: русский язык, машинное обучение, Russian, словообразование, machine learning, language resource, derivational network, derivational morphology, лингвистический ресурс, словообразовательная сеть

In book

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)

Marseille: European Language Resources Association (ELRA), 2022.