Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform
The goal of this paper is to offer a model to quantify the level of complexity of the linguistic content of a corpus in Italian extracted from OpenWHO, WHO’s health emergency learning platform (Rohloff et al. 2018; Zhao et al. 2019). The nature of the computational ranking costs of a typology of re...
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doaj-695a37d08bc54a3783da9353ac89ded22021-02-03T10:43:23ZdeuVilnius University PressVerbum2029-62232538-87462021-01-011110.15388/Verb.15Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning PlatformGiuseppe Samo0Ursula Yu Zhao1Gaya Gamhewage2Beijing Language and Culture University, ChinaWorld Health OrganizationWorld Health Organization The goal of this paper is to offer a model to quantify the level of complexity of the linguistic content of a corpus in Italian extracted from OpenWHO, WHO’s health emergency learning platform (Rohloff et al. 2018; Zhao et al. 2019). The nature of the computational ranking costs of a typology of relativization strategies is investigated. To reach this goal, the results of the corpus are compared with other three syntactic annotated corpora from Italian belonging to different genres (news, social media, encyclopedic entries, legal). The results show that online learning contents in public health reduce complex structures in syntactic terms. The case study presented here provides a methodology to quantify syntactic and computational complexity in corpus studies. https://www.journals.vu.lt/verbum/article/view/21210SyntaxItalianRelativesCovid-19Learning Platforms |
collection |
DOAJ |
language |
deu |
format |
Article |
sources |
DOAJ |
author |
Giuseppe Samo Ursula Yu Zhao Gaya Gamhewage |
spellingShingle |
Giuseppe Samo Ursula Yu Zhao Gaya Gamhewage Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform Verbum Syntax Italian Relatives Covid-19 Learning Platforms |
author_facet |
Giuseppe Samo Ursula Yu Zhao Gaya Gamhewage |
author_sort |
Giuseppe Samo |
title |
Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform |
title_short |
Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform |
title_full |
Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform |
title_fullStr |
Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform |
title_full_unstemmed |
Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform |
title_sort |
syntactic complexity of learning content in italian for covid-19 frontline responders: a study on who’s emergency learning platform |
publisher |
Vilnius University Press |
series |
Verbum |
issn |
2029-6223 2538-8746 |
publishDate |
2021-01-01 |
description |
The goal of this paper is to offer a model to quantify the level of complexity of the linguistic content of a corpus in Italian extracted from OpenWHO, WHO’s health emergency learning platform (Rohloff et al. 2018; Zhao et al. 2019). The nature of the computational ranking costs of a typology of relativization strategies is investigated. To reach this goal, the results of the corpus are compared with other three syntactic annotated corpora from Italian belonging to different genres (news, social media, encyclopedic entries, legal). The results show that online learning contents in public health reduce complex structures in syntactic terms. The case study presented here provides a methodology to quantify syntactic and computational complexity in corpus studies.
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topic |
Syntax Italian Relatives Covid-19 Learning Platforms |
url |
https://www.journals.vu.lt/verbum/article/view/21210 |
work_keys_str_mv |
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