Current Trends in the Use of Machine Learning for Error Correction in Ukrainian Texts

2024 · doi:10.32388/n4vgbj.2
preprint OA: closed
Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-07, 2026-07-06 · read from full text ⓘ

This paper surveys current trends in the use of machine learning methods for error correction in Ukrainian texts, focusing on how such approaches are applied to text preprocessing and correction tasks. It does not present original experimental results in the provided text; instead, it describes the state of the field and the availability of an updated version. A major limitation is that the supplied excerpt contains only publication metadata and no detailed methods, datasets, or quantitative findings. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

The article's authors have provided a detailed problem description of identifying and correcting errors in Ukrainian-language texts. This paper provides a detailed analysis of the latest research and publications aimed at solving the problems of identifying and correcting errors in Ukrainian-language texts. The analysis of modern tools related to error correction in texts is presented along with a comparative description. The existing data corpora for the Ukrainian language were investigated to ensure they are relevant to solving GEC tasks. The need to create a large annotated data corpus, which will be prepared by a special team with linguistic expertise, was discovered. The opportunities, advantages, and disadvantages of modern machine learning models that interpret the task of detecting and correcting errors in texts as classification or machine translation were analysed. The need to develop a machine-learning algorithm that will take into account the specifics of morphologically complex languages, such as Ukrainian, was introduced. The work of the modern models was demonstrated, and screenshots were provided. The need for further research in the Ukrainian segment of machine learning to solve the problems of correcting errors in texts using various methods and approaches was revealed.
Full text 621 characters · extracted from oa-doi-fallback · click to expand
There is a newer version available for this {{ publicationType }}. View latest version {{ publication.field_name }} {{ publication.subfield_name }} Copyright: © {{ publicationYear }} {{ publication.presentation_authors[0].full_name + (publication.presentation_authors.length > 1 ? ' et al' : '') }}. This is an open access publication distributed under the terms of the CC BY 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Check the {{ publicationType | capitalize }} Source for copyright and license information. Listen on

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00