Prediction of reading difficulty in Russian academic texts

Solovyev, Valery D.; Solnyshkina, Marina; Ivanov, Vladimir; Batyrshin, Ildar

doi:10.3233/jifs-179007

Cited by 19 publications

(12 citation statements)

References 12 publications

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“…For our task, we collected a corpus of Russian schoolbook texts. Some of them were obtained by OCR processing and some were taken from the database provided by the authors of [ 4 , 12 , 13 ] at https://kpfu.ru/slozhnost-tekstov-304364.html . The dataset size and structure are provided in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…The formula is as follows: In 2018 Oborneva’s formula was developed further by V. Solovyev and his colleagues. In the paper [ 13 ] the formula was applied to the Russian Readability Corpus constructed of school textbooks on Social Studies and its coefficients were adjusted. The authors suggested an extended number of features and computed the values of correlation coefficients between each feature and the target variable.…”

Section: Related Workmentioning

confidence: 99%

“…For more than 40 years there have been held numerous studies aiming at defining a proper set of parameters which could become a basis for a universal automated tool for measuring the readability of texts in Russian. In recent years a step forward was made by a series of surveys introduced by V. D. Soloviev, M. I. Solnyshkina and their coauthors [ 4 , 12 , 13 ]. These papers share the authors’ experience of using a wide variety of features for evaluating the complexity of Russian school textbooks on Social Studies and provide several Russian readability linear formulas which showed good quality in application to textbooks and outperformed the older formulas.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Robustness of Reading Difficulty Models for Russian Educational Texts

Isaeva

Sorokin

2021

Communications in Computer and Information Science

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Recent papers on Russian readability suggest several formulas aimed at evaluating text reading difficulty for learners of different ages. However, little is known about individual formulas for school subjects and their performance compared to that of existing universal readability formulas. Our goal is to study the impact of the subject both in terms of model quality and on the importance of individual features. We trained 4 linear regression models: an individual formula for each of 3 school subjects (Biology, Literature, and Social Studies) and a universal formula for all the 3 subjects. The dataset was created of schoolbook texts, randomly sampled into pseudo-texts of size 500 sentences. It was split into train and test sets in the ratio of 75 to 25. As for the features, previous papers on Russian readability do not provide proper feature selection. So we suggested a set of 32 features that are possibly relevant to text difficulty in Russian. For every model, features were selected from this set based on their importance. The results obtained show that all the one-subject formulas outperform the universal model and previously developed readability formulas. Experiments with other sample sizes (200 and 900 sentences per sample) prove these results. This is because feature importances vary significantly among the subjects. Suggested readability models might be beneficial for school education for evaluating text relevance for learners and adjusting those texts to target difficulty levels.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the Robustness of Reading Difficulty Models for Russian Educational Texts

Isaeva

Sorokin

2021

Communications in Computer and Information Science

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“…The current study is a part of an on-going Project initiated by Kazan Federal University in 2017 and aimed at evaluating the quality and text complexity of textbooks used in the Russian Federation Solovyev et al, 2019a;Solovyev et al, 2019b;Petrova, Solnyshkina, 2019]. The Project is outlined in three stages: (1) testing participants' reading comprehension proficiency in Russian and the language of the textbooks they use;…”

Section: Studymentioning

confidence: 99%

The Russian Language Test: Towards Assessing Text Comprehension

McCarthy

McNamara

Solnyshkina

et al. 2019

Vestn. Volgogr. gos. univ. Ser. 2 Jazykozn.

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Reading comprehension relies on a variety of complex skills that are not effectively assessed by existing Russian language tests. At the same time, Russian textbooks are criticized both for their low text quality and high text complexity. This study addresses issues of Russian language proficiency and comprehension assessment with the development of the Russian Language Test (RLT). The RLT was constructed to measure proficiency relevant to textbook comprehension, such as grammar and vocabulary knowledge, establishing propositional meaning and inferencing. Results from this initial study including 81 fifth-grade and 94 ninth-grade students confirm that students struggle with grammatical inferences and identifying the main idea in a text. Additionally, three standardized Russian exams, VPR, OGE, EGE are analyzed, affording an overview of the testing system for the Russian language from the elementary through high school education levels. This study demonstrates promise for the use of the RLT as a language proficiency assessment and provides a broad context for understanding the current state of Russian language tests for native speakers.

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“…18. № 3 ющимися проблемами восприятия и сложности текста (см., например: [Solovyev et al, 2019]). В российской парадигме лингвистики текста, весьма динамично развивающейся на данном этапе, используются четыре основных термина, интенсионалы которые во многом совпадают: «сложность», «читабельность», «трудность», «понятность» (см., например: [Солнышкина, Кисельников, 2015]).…”

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Writing Communication: Textual and Linguistic Approaches to Theory and Empirism (Book Review: Textkommunikation. Ein textlinguistischer Neuansatz zur Theorie und Empirie der Kommunikation mit und durch Schrift [Text] / H. Hausendorf, W. Kesselheim, H. Kato, M. Breitholz. – Berlin ; Boston : De Gruyter, 2017. – 414 S.)

Pétrova

Solnyshkina

2019

Vestn. Volgogr. gos. univ. Ser. 2 Jazykozn.

Self Cite

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Prediction of reading difficulty in Russian academic texts

Cited by 19 publications

References 12 publications

Investigating the Robustness of Reading Difficulty Models for Russian Educational Texts

Investigating the Robustness of Reading Difficulty Models for Russian Educational Texts

The Russian Language Test: Towards Assessing Text Comprehension

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