Estimating cognitive text complexity with aggregation of quantile-based models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A3KLG9ANG" target="_blank" >RIV/00216208:11320/23:3KLG9ANG - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175547918&doi=10.28995%2f2075-7182-2023-22-525-538&partnerID=40&md5=8eea1c304a8f0a5bae343687a9a47675" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175547918&doi=10.28995%2f2075-7182-2023-22-525-538&partnerID=40&md5=8eea1c304a8f0a5bae343687a9a47675</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.28995/2075-7182-2023-22-525-538" target="_blank" >10.28995/2075-7182-2023-22-525-538</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating cognitive text complexity with aggregation of quantile-based models

Popis výsledku v původním jazyce

"In this paper, we introduce a novel approach to estimating the cognitive complexity of a text at different levels of language: phonetic, morphemic, lexical, and syntactic. The proposed method detects tokens with an abnormal frequency of complexity scores. The frequencies are taken from the empirical distributions calculated over the reference corpus of texts. We use the Russian Wikipedia for this purpose. Ensemble models are combined from individual models from different language levels. We created datasets of pairs of text fragments taken from social studies textbooks of different grades to train the ensembles. Empirical evidence shows that the proposed approach outperforms existing methods, such as readability indices, in estimating text complexity in terms of accuracy. The purpose of this study is to create one of the important components of the system of recommendation of scientific and educational content. © Dialogue 2023.All rights reserved."

Název v anglickém jazyce

Estimating cognitive text complexity with aggregation of quantile-based models

Popis výsledku anglicky

"In this paper, we introduce a novel approach to estimating the cognitive complexity of a text at different levels of language: phonetic, morphemic, lexical, and syntactic. The proposed method detects tokens with an abnormal frequency of complexity scores. The frequencies are taken from the empirical distributions calculated over the reference corpus of texts. We use the Russian Wikipedia for this purpose. Ensemble models are combined from individual models from different language levels. We created datasets of pairs of text fragments taken from social studies textbooks of different grades to train the ensembles. Empirical evidence shows that the proposed approach outperforms existing methods, such as readability indices, in estimating text complexity in terms of accuracy. The purpose of this study is to create one of the important components of the system of recommendation of scientific and educational content. © Dialogue 2023.All rights reserved."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

"Komp'ut. Lingvist. Intellekt. Tehnol."

ISBN

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ISSN

2221-7932

e-ISSN

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Počet stran výsledku

15

Strana od-do

539-553

Název nakladatele

ABBYY PRODUCTION LLC

Místo vydání

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Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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