{"title":"Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform","authors":"Giuseppe Samo, Ursula Zhao, G. Gamhewage","doi":"10.15388/VERB.15","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":42449,"journal":{"name":"Verbum","volume":null,"pages":null},"PeriodicalIF":0.1000,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Verbum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15388/VERB.15","RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HISTORY","Score":null,"Total":0}
引用次数: 2
Abstract
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.
本文的目标是提供一个模型来量化从世界卫生组织的卫生紧急情况学习平台OpenWHO提取的意大利语语料库的语言内容的复杂性水平（Rohloff et al.2018；赵et al.2019）。研究了相对化策略类型的计算排序成本的性质。为了达到这一目标，将语料库的结果与其他三个来自不同流派（新闻、社交媒体、百科全书条目、法律）的意大利语句法注释语料库进行了比较。结果表明，公共卫生领域的在线学习内容减少了句法方面的复杂结构。本文的案例研究提供了一种量化语料库研究中句法和计算复杂性的方法。
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