Python spelling corrector4/16/2023 ![]() A universal scheme for creating an error correction system for different languages is proposed. ![]() A machine learning model for error correction in Ukrainian-language texts has been developed. The neural network’s use with a new architecture, a review of state-of-the-art methods, and a comparison of different pipeline stages will make it possible to determine such a combination of them, allowing a high-quality error correction model in Ukrainian-language texts. For this study, machine learning algorithms were selected when developing a system for correcting errors in Ukrainianlanguage texts using an optimal pipeline, including pre-processing and selecting text content and generating features in small annotated data corpora. Objective of the study is to develop a technology for correcting errors in Ukrainian-language texts based on machine learning methods using a small set of annotated parallel data. Manual data annotation requires a lot of effort by professional linguists, which makes the creation of text corpora, especially in morphologically rich languages, mainly Ukrainian, a time- and resource-consuming process. A large amount of parallel or manually labelled data is required to build a high-quality machine learning model for correcting grammatical/stylistic errors in the texts of those morphologically complex languages. Systems for the English language are constantly developing and currently actively use machine learning methods: classification (sequence tagging) and machine translation. ![]() Unfortunately, there are few studies on other languages. ![]() Thanks to the availability of large data sets, a significant increase in the accuracy of English grammar correction has been achieved. Most research in grammatical and stylistic error correction focuses on error correction in English-language textual content. ![]()
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