The paper addresses the task of taxonomy expansion – hierarchical structures for organizing concepts. We propose an architecture based on the ReAct (Reasoning + Acting) approach that enables taxonomy expansion in a zero-shot setting without fine-tuning large language models. The system is implemented in two scenarios: autonomous navigation from root nodes and verification of hypotheses generated by other models. Experiments on the diachronic RuWordNet dataset show that direct navigation from the root faces limitations due to graph complexity (MAP@3 = 24.6%). However, using the system as a verifier improves the performance of baseline models: MAP@3 gains of 9.5 pp for FastText and 1.1 pp for TaxoYandexGPT-5-Lite. The key advantages of the approach are its universality, the absence of fine-tuning requirements, and interpretability through explicit reasoning chains.

This study is dedicated to experiments in Word Sense Disambiguation on Russian-language material using generative (decoder-only) models of small and medium size. While the direct application of generative models is not an optimal approach for this task, such models demonstrate potential as semantic annotators of large volumes of raw data. The automation of semantic text annotation using generative models may help overcome the bottleneck of insufficient labeled data for training encoder-based models.

Previous studies show that state-of-the-art English and multilingual models can achieve accuracy above 90% on this task, while smaller models typically reach 80%+. The present study aims to determine whether a comparable task can be successfully addressed for small- and medium-scale models adapted to Russian, that do not require substantial computational resources.

The experiments reported in this paper are conducted both in a basic setting (one/few-shot prompting) with separate tests for narrow and broad context windows of the ambiguous lexeme, as well as under several modifications, such as context enrichment with paradigmatic information (e.g., hypernyms, hyponyms, domain labels of the target word) and ensemble approaches in which one model validates and refines the predictions of another. The study is based on the Russian annotated resource RuSemCor, annotated in terms of the RuWordNet semantic net.

The experiments ultimately pursue the development of an efficient and accessible pipeline for automatic semantic annotation of Russian texts, useful both for direct application and for preparing annotated data for machinelearning tasks.