KFU. Card publication. Computing syntactic parameters for automated text complexity assessment

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

COMPUTING SYNTACTIC PARAMETERS FOR AUTOMATED TEXT COMPLEXITY ASSESSMENT

Form of presentation

Articles in international journals and collections

Year of publication

2019

Язык

английский

Authors, employees of KFU

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

Full metadata record

Field DC	Value	Language
dc.contributor.author	Solnyshkina Marina Ivanovna	ru_RU
dc.contributor.author	Solovev Valeriy Dmitrievich	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/eng/?p_id=214668&p_lang=2	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	Articles in international journals and collections	ru_RU