The growing need for using small data distinguished by a set of distributional properties becomes all the more apparent in the era of large language models (LLM). In this paper, we show that for the lemmatisation of the web as corpora texts, heterogeneous social media texts, and dialect texts, the morphological tagging by a model trained on a small dataset with specific properties generally works better than the morphological tagging by a model trained on a large dataset. The material we use is Russian non-standard texts and interviews with dialect speakers. The sequence-to-sequence lemmatisation with the help of taggers trained on smaller linguistically aware datasets achieves the average results of 85 to 90 per cent. These results are consistently (but not always), by 1-2 per cent. higher than the results of lemmatisation with the help of the large-dataset-trained taggers. We analyse these results and outline the possible further research directions.

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.

The Bank of Russian Constructions and Valencies (Russian FrameBank) is an annotation project that takes as input samples from the Russian National Corpus (http://www.ruscorpora.ru). Since Russian verbs and predicates from other POS classes have their particular and not always predictable case pattern, these words and their argument structures are to be described as lexical constructions. The slots of partially filled phrasal constructions (e.g. vzjal i uexal ‘he suddenly (lit. took and) went away’) are also under analysis. Thus, the notion of construction is understood in the sense of Fillmore’s Construction Grammar and is not limited to that of argument structure of verbs. FrameBank brings together the dictionary of constructions and the annotated collection of examples. Our goal is to mark the set of arguments and adjuncts of a certain construction. The main focus is on realization of the elements in the running text, to facilitate searches through pattern realizations by a certain combination of features. The relevant dataset involves lexical, POS and other morphosyntactic tags, semantic classes, as well as grammatical constructions that introduce or license the use of elements within a given construction.