A novel data augmentation method – paraphrastic concept embeddings – is presented, designed to address the problem of insufficient labeled data in Azerbaijani natural language processing (NLP). This method generates high-quality paraphrastic sentences by encoding semantic concepts into a continuous vector space and decoding them into diverse textual realizations. This approach is the first to utilize concept-level paraphrasing for the Azerbaijani language, yielding substantial improvements in applied tasks. The theoretical foundations of the method, including its mathematical formulation and implementation within NLP pipelines, are proposed. In text classification experiments, the method outperforms standard augmentation techniques in accuracy and robustness. The method does not require external lexical resources, making it especially useful for low-resource languages. It scales for various types of tasks, including sentiment analysis, entity extraction and text generation. It is concluded that the proposed approach significantly advances the level of Azerbaijani NLP and has the potential to be extended to other low-resource languages.