Kazan (Volga region) Federal University, KFU
KAZAN
FEDERAL UNIVERSITY
 
COMPUTING SYNTACTIC PARAMETERS FOR AUTOMATED TEXT COMPLEXITY ASSESSMENT
Form of presentationArticles in international journals and collections
Year of publication2019
Языканглийский
  • Solnyshkina Marina Ivanovna, author
  • Solovev Valeriy Dmitrievich, author
  • Bibliographic description in the original language Solovyev V, Solnyshkina M, Ivanov V, Computing syntactic parameters for automated text complexity assessment//CEUR Workshop Proceedings. - 2019. - Vol.2475, Is.. - P.62-71.
    Annotation The article focuses on identifying, extracting and evaluating syntactic parameters influencing the complexity of Russian academic texts. Our ultimate goal is to select a set of text features effectively measuring text complexity and build an automatic tool able to rank Russian academic texts according to grade levels. models based on the most promising features by using machine learning methods The innovative algorithm of designing a predictive model of text complexity is based on a training text corpus and a set of previously proposed and new syntactic features (average sentence length, average number of syllables per word, the number of adjectives, average number of participial constructions, average number of coordinating chains, path number, i.e. average number of sub-trees). Our best model achieves an MSE of 1.15. Our experiments indicate that by adding the abovementioned syntactic features, namely the average number of participial constructions, average number of coordinating chains, and the average number of sub-trees, the text complexity model performance will increase substantially
    Keywords reading comprehension
    The name of the journal CEUR Workshop Proceedings
    URL https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074072027&partnerID=40&md5=b6a5b738cb7fe812903fb259b711dbe4
    Please use this ID to quote from or refer to the card https://repository.kpfu.ru/eng/?p_id=214668&p_lang=2

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