The article introduces a novel task of multi-lect automatic detection of Swadesh list items from raw corpora. The task aids the early stageof historical linguistics study by helping the researcher compile word lists for further analysis.In this paper, I test multi-lect automatic detection on the East Slavic lects’ data. The training data consists of Ukrainian, Belarusian, and Russian material. I introduce a new dataset for the Ukrainian language. I implement data augmentation techniques to give automatic tools a better understanding of the searched value. The test data consists of the Old East Slavic texts.I train HMM, CRF, and mBERT models, then test and evaluate them by harmonic F1 score. The baseline is a Random Forest classifier. I introduce two different subtasks: the search for new Swadesh list items, and the search for the known Swadesh list items in new lects of the well-established group. The first subtask, given the simultaneously diverse and vague nature of the Swadesh list, currently presents an almost unbeatable challenge for machine learning methods. The second subtask, on the other hand, is easier, and the mBERT model achieves a 0.57 F1 score. This is an impressive result, given how hard it is to formalise the token belonging to a very specific and thematically diverse set of concepts.

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.

The study of low-resourced East Slavic lects is becoming increasingly relevant as they face the prospect of extinction under the pressure of standard Russian while being treated by academia as an inferior part of this lect. The Khislavichi lect, spoken in a settlement on the border of Russia and Belarus, is a perfect example of such an attitude. We take an alternative approach and study East Slavic lects (such as Khislavichi) as separate systems. The proposed method includes the development of a tagged corpus through morphological tagging with the models trained on the bigger lects. Morphological tagging results may be used to place these lects among the bigger ones, such as standard Belarusian or standard Russian. The implemented morphological taggers of standard Russian and standard Belarusian demonstrate an accuracy higher than the accuracy of multilingual models by 3 to 15%. The study suggests possible ways to adapt these taggers to the Khislavichi dataset, such as tagset unification and transcription closer to the actual sound rather than the standard lect pronunciation. Automatic classification supports the hypothesis that Khislavichi is a border East Slavic lect that historically was Belarusian but got russified: the algorithm places it either slightly closer to Russian or to Belarusian.