Classification of Text Writing Proficiency of L2 Learners
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A2HVQTCK3" target="_blank" >RIV/00216208:11320/23:2HVQTCK3 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164949611&doi=10.1007%2f978-3-031-37105-9_2&partnerID=40&md5=297ee5affa2045037cc5c5a0b42f22c4" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164949611&doi=10.1007%2f978-3-031-37105-9_2&partnerID=40&md5=297ee5affa2045037cc5c5a0b42f22c4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-37105-9_2" target="_blank" >10.1007/978-3-031-37105-9_2</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Classification of Text Writing Proficiency of L2 Learners
Popis výsledku v původním jazyce
"In this study, we present a novel system for the automatic classification of text complexity in the Italian language, focusing on the phraseological dimension. This quantitative assessment of text complexity is crucial for various applications, including text readability measurement, text simplification, and support for educators during evaluation processes. We use a dataset comprising texts written by Italian L2 learners and classified according to the levels of the Common European Framework of Reference for Languages. The dataset texts serve as a basis for calculating phraseological features, which are then used as input for multiple machine-learning classifiers to compare their performance in predicting proficiency levels. Our experimental results demonstrate that the proposed framework effectively harnesses phraseological complexity features to achieve high classification accuracy in determining proficiency levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG."
Název v anglickém jazyce
Classification of Text Writing Proficiency of L2 Learners
Popis výsledku anglicky
"In this study, we present a novel system for the automatic classification of text complexity in the Italian language, focusing on the phraseological dimension. This quantitative assessment of text complexity is crucial for various applications, including text readability measurement, text simplification, and support for educators during evaluation processes. We use a dataset comprising texts written by Italian L2 learners and classified according to the levels of the Common European Framework of Reference for Languages. The dataset texts serve as a basis for calculating phraseological features, which are then used as input for multiple machine-learning classifiers to compare their performance in predicting proficiency levels. Our experimental results demonstrate that the proposed framework effectively harnesses phraseological complexity features to achieve high classification accuracy in determining proficiency levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG."
Klasifikace
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)
Návaznosti výsledku
Projekt
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Návaznosti
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Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
"Lect. Notes Comput. Sci."
ISBN
978-303137104-2
ISSN
0302-9743
e-ISSN
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Počet stran výsledku
14
Strana od-do
15-28
Název nakladatele
Springer Science and Business Media Deutschland GmbH
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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