| Форма представления | Статьи в зарубежных журналах и сборниках |
| Год публикации | 2019 |
| Язык | английский |
|
Солнышкина Марина Ивановна, автор
Соловьев Валерий Дмитриевич, автор
|
| Библиографическое описание на языке оригинала |
Solovyev V, Solnyshkina M, Ivanov V, Computing syntactic parameters for automated text complexity assessment//CEUR Workshop Proceedings. - 2019. - Vol.2475, Is.. - P.62-71. |
| Аннотация |
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 |
| Ключевые слова |
reading comprehension |
| Название журнала |
CEUR Workshop Proceedings
|
| URL |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074072027&partnerID=40&md5=b6a5b738cb7fe812903fb259b711dbe4 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=214668 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Солнышкина Марина Ивановна |
ru_RU |
| dc.contributor.author |
Соловьев Валерий Дмитриевич |
ru_RU |
| dc.date.accessioned |
2019-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2019-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2019 |
ru_RU |
| dc.identifier.citation |
Solovyev V, Solnyshkina M, Ivanov V, Computing syntactic parameters for automated text complexity assessment//CEUR Workshop Proceedings. - 2019. - Vol.2475, Is.. - P.62-71. |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/?p_id=214668 |
ru_RU |
| dc.description.abstract |
CEUR Workshop Proceedings |
ru_RU |
| dc.description.abstract |
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 |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
|
ru_RU |
| dc.title |
Computing syntactic parameters for automated text complexity assessment |
ru_RU |
| dc.type |
Статьи в зарубежных журналах и сборниках |
ru_RU |
|
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