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Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
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Документ The Influence of Various Text Characteristics on the Readability and Content Informativeness(2019) Khairova, N. F.; Kolesnyk, Anastasiia; Mamyrbayev, Orken; Mukhsina, KuralayCurrently, businesses increasingly use various external big data sources for extracting and integrating information into their own enterprise information systems to make correct economic decisions, to understand customer needs, and to predict risks. The necessary condition for obtaining useful knowledge from big data is analysing high-quality data and using quality textual data. In the study, we focus on the influence of readability and some particular features of the texts written for a global audience on the texts quality assessment. In order to estimate the influence of different linguistic and statistical factors on the text readability, we reviewed five different text corpora. Two of them contain texts from Wikipedia, the third one contains texts from Simple Wikipedia and two last corpora include scientific and educational texts. We show linguistic and statistical features of a text that have the greatest influence on the text quality for business corporations. Finally, we propose some directions on the way to automatic predicting the readability of texts in the Web.Документ The aligned Kazakh-Russian parallel corpus focused on the criminal theme(2019) Khairova, N. F.; Kolesnyk, Anastasiia; Mamyrbayev, Orken; Mukhsina, KuralayNowadays, the development of high-quality parallel aligned text corpora is one of the most relevant and advanced directions of modern linguistics. Special emphasis is placed in creating parallel multilingual corpora for low resourced languages, such as the Kazakh language. In the study, we explored texts from four Kazakh bilingual news websites and created the parallel Kazakh-Russian corpus of texts that focus on the criminal subject at their base. In order to align the corpus, we used lexical compliances set and the values of POS-tagging of both languages. 60% of our corpus sentences are automatically aligned correctly. Finally, we analyzed the factors affecting the percentage of errors.